https://rctn.org/w/api.php?action=feedcontributions&feedformat=atom&user=DpaitonRedwoodCenter - User contributions [en]2026-06-13T19:17:17ZUser contributionsMediaWiki 1.39.4https://rctn.org/w/index.php?title=People_at_the_Redwood_Center&diff=8995People at the Redwood Center2017-09-05T22:23:45Z<p>Dpaiton: </p>
<hr />
<div>__NOEDITSECTION__<br />
== Faculty ==<br />
<br />
[[Image:deweese.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Michael R. DeWeese''', Associate Professor, HWNI and Physics <br /><br />
[http://physics.berkeley.edu/people/faculty/michael-deweese Physics home page] <br /><br />
[http://redwood.berkeley.edu/wiki/Michael_DeWeese publications]<br />
<br />
[[Image:bruno.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Bruno Olshausen''', Director and Professor, HWNI and School of Optometry <br /><br />
[http://redwood.berkeley.edu/bruno home page]<br />
<br />
<br />
[[Image:fritz.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Fritz Sommer''', Acting Director and Associate Adjunct Professor, HWNI <br /><br />
[[Fritz Sommer|home page]]<br />
<br />
== Research Scientists ==<br />
<br style="clear:both;" /><br />
'''Pentti Kanerva''' <br /><br />
[[Pentti Kanerva|home page]]<br />
<br />
'''Vivienne L'Ecuyer Ming''', Visiting Scholar<br /><br />
[http://www.vivienneming.com home page]<br />
<br />
[[Image:bnfcjh.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Chris Hillar''' <br /><br />
[http://www.msri.org/people/members/chillar/ home page]<br />
<br />
<br />
[[Image:Allie_photo.jpeg]]<br />
<br style="clear:both;" /><br />
'''Alyson "Allie" Fletcher''' <br/><br />
Affiliated Researcher & UCSC Professor <br/><br />
[http://users.soe.ucsc.edu/~afletcher/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Karl Zipser''' <br/><br />
Assistant Researcher<br/><br />
[http://karlzipser.com home page]<br />
<br />
[[Image:Mika_photo.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
<b>Mika Laiho</b> <br /><br />
Visiting Scholar <br /><br />
[http://trc.utu.fi/microelectronics/research/emerging-computing-schemes Website]<br />
<br />
== Postdocs ==<br />
'''Petr Ježek''' <br /><br />
[[Petr Jezek|home page]]<br />
<br />
== Staff ==<br />
[[Image:JeffTeeters.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Jeff teeters''' <br /><br />
[[Jeff Teeters|home page]]<br />
<br />
== Students ==<br />
[[File:Neha_profile.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Neha Wadia''' <br />
[http://redwood.berkeley.edu/w/images/0/01/Neha_resume.pdf CV]<br />
<br />
[[File:Alex.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Alex Anderson''' <br /><br />
[http://www.ocf.berkeley.edu/~aga/ Website]<br />
<br />
[[File:JamesA.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''James Arnemann''' <br /><br />
[[James Arnemann|home page]]<br />
<br />
'''Brian Cheung''' <br /><br />
[[Brian Cheung|home page]]<br />
<br />
[[File:dylan_headshot.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Dylan Paiton''' <br /><br />
[http://vision.berkeley.edu/?p=3052 home page]<br />
<br />
[[File:yubei.png|150px|left]]<br />
<br style="clear:both;" /><br />
'''Yubei Chen''' <br /><br />
[[Yubei Chen|home page]]<br />
<br />
'''Guy Isely''' <br /><br />
[http://gisely.github.io/ home page]<br />
<br />
[[File:spencer_redwood_people.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Spencer Kent''' <br /><br />
[http://www.spencerkent.com home page]<br />
<br />
[[File:Shariq222.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Shariq Mobin''' <br /><br />
[http://shariqmobin.weebly.com/ home page]<br />
<br />
[[Image:mayur_mudigonda.jpg|alt=Mayur: the Magical Machine Learning Pony!|left]]<br />
<br style="clear:both;" /><br />
'''Mayur Mudigonda''' <br /><br />
[[http://redwood.berkeley.edu/mayur home page]]<br />
<br />
[[File:ogre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Chris Warner''' <br /><br />
[https://redwood.berkeley.edu/wiki/User:Cwarner home page]<br />
<br />
[[File:eric.JPG|150px|left]]<br />
<br style="clear:both;" /><br />
'''Eric Weiss''' <br /><br />
[[Eric Weiss|home page]]<br />
<br />
[[File:Pooneh.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pooneh Mohammadiara''' <br/><br />
[[Pooneh Mohammadiara| home page]]<br />
<br />
[[File:Vasha.jpeg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Vasha DuTell''' <br/><br />
[[Vasha DuTell| home page]]<br />
<br />
== Alumni ==<br />
<br />
'''Jesse Livezey''' <br /><br />
[http://jesselivezey.com/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Alex Bujan''' <br /><br />
[http://afbujan.github.io/ Website]<br />
<br />
'''Chayut Thanapirom''' <br /><br />
[[Chayut Thanapirom|home page]]<br />
<br />
[[Image:tony.jpg|170px|left]]<br />
<br style="clear:both;" /><br />
'''Tony Bell''' <br /><br />
[[Tony Bell|home page]]<br />
<br />
[[Image:tim.jpg]] <br />
<br style="clear:both;" /><br />
'''Tim Blanche''' <br /><br />
[[Tim Blanche|home page]]<br />
<br />
[[Image:kilian.jpg|115px|left|link=Kilian Koepsell|Kilian Koepsell]]<br />
<br style="clear:both;" /><br />
'''Kilian Koepsell''' <br /><br />
[http://redwood.berkeley.edu/kilian/ home page] <br /><br />
[http://redwood.berkeley.edu/klab/ lab page]<br />
<br />
'''Mat Leonard''' <br /><br />
[[Mat Leonard|home page]]<br />
<br />
'''Joseph Thurakal''' <br /><br />
[[Joseph Thurakal|home page]]<br />
<br />
'''Sarah Marzen''' <br /><br />
[[Sarah Marzen|home page]]<br />
<br />
<br style="clear:both;" /><br />
'''Urs Koester''' <br /><br />
[[Urs | home page]]<br />
<br />
[[File:Gautam.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Gautam Agarwal''' <br /><br />
Postdoc at the Champalimaud Centre for the Unknown<br /><br />
[http://www.neuro.fchampalimaud.org/en/research/investigators/research-groups/group/Mainen/ home page]<br />
<br />
[[Image:ian.jpg|130px|left]]<br />
<br style="clear:both;" /><br />
'''Ian Stevenson''' <br /><br />
Currently at University of Connecticut<br /><br />
[http://homepages.uconn.edu/~ias13002/ home page]<br />
<br />
[[Image:paul_ivanov.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Paul Ivanov''' <br /><br />
[http://pirsquared.org/ home page]<br />
<br />
'''Daniel Little''' <br /><br />
[[Daniel Little|home page]]<br />
<br />
'''Chris Rodgers''' <br /><br />
[http://chris-rodgers.com/ home page]<br />
<br />
'''Sharif Corinaldi''' <br /><br />
[[Sharif Corinaldi|home page]]<br />
<br />
'''Amir Khosrowshahi''' <br /><br />
[[Amir Khosrowshahi|home page]]<br />
<br />
'''Antony Lee''' <br /><br />
[[Antony Lee|home page]]<br />
<br />
'''Chetan Nandakumar''' <br /><br />
[[Chetan Nandakumar|home page]]<br />
<br />
'''Jascha Sohl-Dickstein''' <br /><br />
[[Jascha Sohl-Dickstein|home page]]<br />
<br />
[[Image:jiminy.gif|left]]<br />
<br style="clear:both;" /><br />
'''Jimmy Wang''' <br /><br />
[https://redwood.berkeley.edu/jwang/index.html home page]<br />
<br />
'''Joel Zylberberg''' <br /><br />
[[Joel Zylberberg|home page]]<br />
<br />
'''Jack Culpepper''' <br /><br />
[http://www.cs.berkeley.edu/~bjc/ home page]<br />
<br />
'''Badr Faisal Albanna''' <br /><br />
[[Badr Faisal Albanna|home page]]<br />
<br />
'''Peter Battaglino''' <br /><br />
[[Peter Battaglino|home page]]<br />
<br />
[[Image:nicole.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Nicole Carlson''' <br /><br />
[[Nicole Carlson|home page]]<br />
<br />
'''Alfonso Apicella''' <br /><br />
[[Alfonso Apicella|home page]]<br />
<br />
[[Image:charles.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Charles Cadieu''' <br /><br />
[http://charles.cadieu.us home page]<br />
<br />
[[Image:NicolFace.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Nicol Harper''' <br /><br />
[[Nicol Harper|home page]]<br />
<br />
'''Vivek Ayer''' <br /><br />
[[Vivek Ayer|home page]]<br />
<br />
[[Image:matthias.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Matthias Bethge''', Postdoctoral Fellow, 2005 <br /><br />
Currently at the Max-Planck Institute, Tubingen <br /><br />
Winner of the Bernstein Independent Investigator Prize <br /><br />
[http://www.neuro.uni-bremen.de/~mbethge/ home page]<br />
<br />
'''Will Coulter''', MA Neuroscience (2009) <br /><br />
Currently at Siemens Healthcare Diagnostics R&D <br /><br />
[[Will Coulter|home page]] (outdated)<br />
<br />
'''Mohammad Dastjerdi''' <br /><br />
[[Mohammad Dastjerdi|home page]]<br />
<br />
[[Image:pierre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pierre Garrigues''' <br /><br />
[http://redwood.berkeley.edu/pierre home page]<br />
<br />
'''Joe Goldbeck''', MA Neuroscience (2012) <br /><br />
<br />
[[Image:thomas.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Thomas Lauritzen''', Research Scientist, 2005-2010 <br /><br />
Currently at Second Sight Medical Products, Sylmar, CA <br /><br />
[[Thomas Lauritzen|home page]]<br />
<br />
[[Image:Gianluca.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Gianluca Monaci''' <br /><br />
Currently at Philips Research, Eindhoven <br /><br />
[[Gianluca Monaci|home page]]<br />
<br />
'''Martin Rehn''' <br /><br />
[[Martin Rehn|home page]]<br />
<br />
[[Image:Rozell.jpg]]<br />
<br style="clear:both;" /><br />
'''Chris Rozell''' <br /><br />
[[http://www.ece.rice.edu/~crozell/ home page]]<br />
<br />
[[Image:Ivana.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Ivana Tosic''', Postdoctoral researcher, 2009-2011 <br /><br />
Currently with [http://rii.ricoh.com/ Ricoh Innovations Inc.], Menlo Park <br /><br />
[http://ivanatosic.net home page]</div>Dpaitonhttps://rctn.org/w/index.php?title=File:Dylan_headshot.jpg&diff=8994File:Dylan headshot.jpg2017-09-05T22:22:45Z<p>Dpaiton: </p>
<hr />
<div></div>Dpaitonhttps://rctn.org/w/index.php?title=People_at_the_Redwood_Center&diff=8993People at the Redwood Center2017-09-05T22:21:24Z<p>Dpaiton: </p>
<hr />
<div>__NOEDITSECTION__<br />
== Faculty ==<br />
<br />
[[Image:deweese.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Michael R. DeWeese''', Associate Professor, HWNI and Physics <br /><br />
[http://physics.berkeley.edu/people/faculty/michael-deweese Physics home page] <br /><br />
[http://redwood.berkeley.edu/wiki/Michael_DeWeese publications]<br />
<br />
[[Image:bruno.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Bruno Olshausen''', Director and Professor, HWNI and School of Optometry <br /><br />
[http://redwood.berkeley.edu/bruno home page]<br />
<br />
<br />
[[Image:fritz.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Fritz Sommer''', Acting Director and Associate Adjunct Professor, HWNI <br /><br />
[[Fritz Sommer|home page]]<br />
<br />
== Research Scientists ==<br />
<br style="clear:both;" /><br />
'''Pentti Kanerva''' <br /><br />
[[Pentti Kanerva|home page]]<br />
<br />
'''Vivienne L'Ecuyer Ming''', Visiting Scholar<br /><br />
[http://www.vivienneming.com home page]<br />
<br />
[[Image:bnfcjh.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Chris Hillar''' <br /><br />
[http://www.msri.org/people/members/chillar/ home page]<br />
<br />
<br />
[[Image:Allie_photo.jpeg]]<br />
<br style="clear:both;" /><br />
'''Alyson "Allie" Fletcher''' <br/><br />
Affiliated Researcher & UCSC Professor <br/><br />
[http://users.soe.ucsc.edu/~afletcher/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Karl Zipser''' <br/><br />
Assistant Researcher<br/><br />
[http://karlzipser.com home page]<br />
<br />
[[Image:Mika_photo.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
<b>Mika Laiho</b> <br /><br />
Visiting Scholar <br /><br />
[http://trc.utu.fi/microelectronics/research/emerging-computing-schemes Website]<br />
<br />
== Postdocs ==<br />
'''Petr Ježek''' <br /><br />
[[Petr Jezek|home page]]<br />
<br />
== Staff ==<br />
[[Image:JeffTeeters.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Jeff teeters''' <br /><br />
[[Jeff Teeters|home page]]<br />
<br />
== Students ==<br />
[[File:Neha_profile.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Neha Wadia''' <br />
[http://redwood.berkeley.edu/w/images/0/01/Neha_resume.pdf CV]<br />
<br />
[[File:Alex.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Alex Anderson''' <br /><br />
[http://www.ocf.berkeley.edu/~aga/ Website]<br />
<br />
[[File:JamesA.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''James Arnemann''' <br /><br />
[[James Arnemann|home page]]<br />
<br />
'''Brian Cheung''' <br /><br />
[[Brian Cheung|home page]]<br />
<br />
'''Dylan Paiton''' <br /><br />
[https://dpaiton.github.io/static/img/headshot.jpg Image]<br />
[http://vision.berkeley.edu/?p=3052 home page]<br />
<br />
[[File:yubei.png|150px|left]]<br />
<br style="clear:both;" /><br />
'''Yubei Chen''' <br /><br />
[[Yubei Chen|home page]]<br />
<br />
'''Guy Isely''' <br /><br />
[http://gisely.github.io/ home page]<br />
<br />
[[File:spencer_redwood_people.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Spencer Kent''' <br /><br />
[http://www.spencerkent.com home page]<br />
<br />
[[File:Shariq222.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Shariq Mobin''' <br /><br />
[http://shariqmobin.weebly.com/ home page]<br />
<br />
[[Image:mayur_mudigonda.jpg|alt=Mayur: the Magical Machine Learning Pony!|left]]<br />
<br style="clear:both;" /><br />
'''Mayur Mudigonda''' <br /><br />
[[http://redwood.berkeley.edu/mayur home page]]<br />
<br />
[[File:ogre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Chris Warner''' <br /><br />
[https://redwood.berkeley.edu/wiki/User:Cwarner home page]<br />
<br />
[[File:eric.JPG|150px|left]]<br />
<br style="clear:both;" /><br />
'''Eric Weiss''' <br /><br />
[[Eric Weiss|home page]]<br />
<br />
[[File:Pooneh.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pooneh Mohammadiara''' <br/><br />
[[Pooneh Mohammadiara| home page]]<br />
<br />
[[File:Vasha.jpeg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Vasha DuTell''' <br/><br />
[[Vasha DuTell| home page]]<br />
<br />
== Alumni ==<br />
<br />
'''Jesse Livezey''' <br /><br />
[http://jesselivezey.com/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Alex Bujan''' <br /><br />
[http://afbujan.github.io/ Website]<br />
<br />
'''Chayut Thanapirom''' <br /><br />
[[Chayut Thanapirom|home page]]<br />
<br />
[[Image:tony.jpg|170px|left]]<br />
<br style="clear:both;" /><br />
'''Tony Bell''' <br /><br />
[[Tony Bell|home page]]<br />
<br />
[[Image:tim.jpg]] <br />
<br style="clear:both;" /><br />
'''Tim Blanche''' <br /><br />
[[Tim Blanche|home page]]<br />
<br />
[[Image:kilian.jpg|115px|left|link=Kilian Koepsell|Kilian Koepsell]]<br />
<br style="clear:both;" /><br />
'''Kilian Koepsell''' <br /><br />
[http://redwood.berkeley.edu/kilian/ home page] <br /><br />
[http://redwood.berkeley.edu/klab/ lab page]<br />
<br />
'''Mat Leonard''' <br /><br />
[[Mat Leonard|home page]]<br />
<br />
'''Joseph Thurakal''' <br /><br />
[[Joseph Thurakal|home page]]<br />
<br />
'''Sarah Marzen''' <br /><br />
[[Sarah Marzen|home page]]<br />
<br />
<br style="clear:both;" /><br />
'''Urs Koester''' <br /><br />
[[Urs | home page]]<br />
<br />
[[File:Gautam.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Gautam Agarwal''' <br /><br />
Postdoc at the Champalimaud Centre for the Unknown<br /><br />
[http://www.neuro.fchampalimaud.org/en/research/investigators/research-groups/group/Mainen/ home page]<br />
<br />
[[Image:ian.jpg|130px|left]]<br />
<br style="clear:both;" /><br />
'''Ian Stevenson''' <br /><br />
Currently at University of Connecticut<br /><br />
[http://homepages.uconn.edu/~ias13002/ home page]<br />
<br />
[[Image:paul_ivanov.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Paul Ivanov''' <br /><br />
[http://pirsquared.org/ home page]<br />
<br />
'''Daniel Little''' <br /><br />
[[Daniel Little|home page]]<br />
<br />
'''Chris Rodgers''' <br /><br />
[http://chris-rodgers.com/ home page]<br />
<br />
'''Sharif Corinaldi''' <br /><br />
[[Sharif Corinaldi|home page]]<br />
<br />
'''Amir Khosrowshahi''' <br /><br />
[[Amir Khosrowshahi|home page]]<br />
<br />
'''Antony Lee''' <br /><br />
[[Antony Lee|home page]]<br />
<br />
'''Chetan Nandakumar''' <br /><br />
[[Chetan Nandakumar|home page]]<br />
<br />
'''Jascha Sohl-Dickstein''' <br /><br />
[[Jascha Sohl-Dickstein|home page]]<br />
<br />
[[Image:jiminy.gif|left]]<br />
<br style="clear:both;" /><br />
'''Jimmy Wang''' <br /><br />
[https://redwood.berkeley.edu/jwang/index.html home page]<br />
<br />
'''Joel Zylberberg''' <br /><br />
[[Joel Zylberberg|home page]]<br />
<br />
'''Jack Culpepper''' <br /><br />
[http://www.cs.berkeley.edu/~bjc/ home page]<br />
<br />
'''Badr Faisal Albanna''' <br /><br />
[[Badr Faisal Albanna|home page]]<br />
<br />
'''Peter Battaglino''' <br /><br />
[[Peter Battaglino|home page]]<br />
<br />
[[Image:nicole.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Nicole Carlson''' <br /><br />
[[Nicole Carlson|home page]]<br />
<br />
'''Alfonso Apicella''' <br /><br />
[[Alfonso Apicella|home page]]<br />
<br />
[[Image:charles.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Charles Cadieu''' <br /><br />
[http://charles.cadieu.us home page]<br />
<br />
[[Image:NicolFace.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Nicol Harper''' <br /><br />
[[Nicol Harper|home page]]<br />
<br />
'''Vivek Ayer''' <br /><br />
[[Vivek Ayer|home page]]<br />
<br />
[[Image:matthias.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Matthias Bethge''', Postdoctoral Fellow, 2005 <br /><br />
Currently at the Max-Planck Institute, Tubingen <br /><br />
Winner of the Bernstein Independent Investigator Prize <br /><br />
[http://www.neuro.uni-bremen.de/~mbethge/ home page]<br />
<br />
'''Will Coulter''', MA Neuroscience (2009) <br /><br />
Currently at Siemens Healthcare Diagnostics R&D <br /><br />
[[Will Coulter|home page]] (outdated)<br />
<br />
'''Mohammad Dastjerdi''' <br /><br />
[[Mohammad Dastjerdi|home page]]<br />
<br />
[[Image:pierre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pierre Garrigues''' <br /><br />
[http://redwood.berkeley.edu/pierre home page]<br />
<br />
'''Joe Goldbeck''', MA Neuroscience (2012) <br /><br />
<br />
[[Image:thomas.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Thomas Lauritzen''', Research Scientist, 2005-2010 <br /><br />
Currently at Second Sight Medical Products, Sylmar, CA <br /><br />
[[Thomas Lauritzen|home page]]<br />
<br />
[[Image:Gianluca.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Gianluca Monaci''' <br /><br />
Currently at Philips Research, Eindhoven <br /><br />
[[Gianluca Monaci|home page]]<br />
<br />
'''Martin Rehn''' <br /><br />
[[Martin Rehn|home page]]<br />
<br />
[[Image:Rozell.jpg]]<br />
<br style="clear:both;" /><br />
'''Chris Rozell''' <br /><br />
[[http://www.ece.rice.edu/~crozell/ home page]]<br />
<br />
[[Image:Ivana.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Ivana Tosic''', Postdoctoral researcher, 2009-2011 <br /><br />
Currently with [http://rii.ricoh.com/ Ricoh Innovations Inc.], Menlo Park <br /><br />
[http://ivanatosic.net home page]</div>Dpaitonhttps://rctn.org/w/index.php?title=People_at_the_Redwood_Center&diff=8992People at the Redwood Center2017-09-05T22:12:01Z<p>Dpaiton: </p>
<hr />
<div>__NOEDITSECTION__<br />
== Faculty ==<br />
<br />
[[Image:deweese.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Michael R. DeWeese''', Associate Professor, HWNI and Physics <br /><br />
[http://physics.berkeley.edu/people/faculty/michael-deweese Physics home page] <br /><br />
[http://redwood.berkeley.edu/wiki/Michael_DeWeese publications]<br />
<br />
[[Image:bruno.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Bruno Olshausen''', Director and Professor, HWNI and School of Optometry <br /><br />
[http://redwood.berkeley.edu/bruno home page]<br />
<br />
<br />
[[Image:fritz.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Fritz Sommer''', Acting Director and Associate Adjunct Professor, HWNI <br /><br />
[[Fritz Sommer|home page]]<br />
<br />
== Research Scientists ==<br />
<br style="clear:both;" /><br />
'''Pentti Kanerva''' <br /><br />
[[Pentti Kanerva|home page]]<br />
<br />
'''Vivienne L'Ecuyer Ming''', Visiting Scholar<br /><br />
[http://www.vivienneming.com home page]<br />
<br />
[[Image:bnfcjh.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Chris Hillar''' <br /><br />
[http://www.msri.org/people/members/chillar/ home page]<br />
<br />
<br />
[[Image:Allie_photo.jpeg]]<br />
<br style="clear:both;" /><br />
'''Alyson "Allie" Fletcher''' <br/><br />
Affiliated Researcher & UCSC Professor <br/><br />
[http://users.soe.ucsc.edu/~afletcher/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Karl Zipser''' <br/><br />
Assistant Researcher<br/><br />
[http://karlzipser.com home page]<br />
<br />
[[Image:Mika_photo.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
<b>Mika Laiho</b> <br /><br />
Visiting Scholar <br /><br />
[http://trc.utu.fi/microelectronics/research/emerging-computing-schemes Website]<br />
<br />
== Postdocs ==<br />
'''Petr Ježek''' <br /><br />
[[Petr Jezek|home page]]<br />
<br />
== Staff ==<br />
[[Image:JeffTeeters.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Jeff teeters''' <br /><br />
[[Jeff Teeters|home page]]<br />
<br />
== Students ==<br />
[[File:Neha_profile.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Neha Wadia''' <br />
[http://redwood.berkeley.edu/w/images/0/01/Neha_resume.pdf CV]<br />
<br />
[[File:Alex.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Alex Anderson''' <br /><br />
[http://www.ocf.berkeley.edu/~aga/ Website]<br />
<br />
[[File:JamesA.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''James Arnemann''' <br /><br />
[[James Arnemann|home page]]<br />
<br />
'''Brian Cheung''' <br /><br />
[[Brian Cheung|home page]]<br />
<br />
'''Dylan Paiton''' <br /><br />
[https://dpaiton.github.io/static/img/headshot.jpg|150px|left]<br />
[http://vision.berkeley.edu/?p=3052 home page]<br />
<br />
[[File:yubei.png|150px|left]]<br />
<br style="clear:both;" /><br />
'''Yubei Chen''' <br /><br />
[[Yubei Chen|home page]]<br />
<br />
'''Guy Isely''' <br /><br />
[http://gisely.github.io/ home page]<br />
<br />
[[File:spencer_redwood_people.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Spencer Kent''' <br /><br />
[http://www.spencerkent.com home page]<br />
<br />
[[File:Shariq222.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Shariq Mobin''' <br /><br />
[http://shariqmobin.weebly.com/ home page]<br />
<br />
[[Image:mayur_mudigonda.jpg|alt=Mayur: the Magical Machine Learning Pony!|left]]<br />
<br style="clear:both;" /><br />
'''Mayur Mudigonda''' <br /><br />
[[http://redwood.berkeley.edu/mayur home page]]<br />
<br />
[[File:ogre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Chris Warner''' <br /><br />
[https://redwood.berkeley.edu/wiki/User:Cwarner home page]<br />
<br />
[[File:eric.JPG|150px|left]]<br />
<br style="clear:both;" /><br />
'''Eric Weiss''' <br /><br />
[[Eric Weiss|home page]]<br />
<br />
[[File:Pooneh.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pooneh Mohammadiara''' <br/><br />
[[Pooneh Mohammadiara| home page]]<br />
<br />
[[File:Vasha.jpeg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Vasha DuTell''' <br/><br />
[[Vasha DuTell| home page]]<br />
<br />
== Alumni ==<br />
<br />
'''Jesse Livezey''' <br /><br />
[http://jesselivezey.com/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Alex Bujan''' <br /><br />
[http://afbujan.github.io/ Website]<br />
<br />
'''Chayut Thanapirom''' <br /><br />
[[Chayut Thanapirom|home page]]<br />
<br />
[[Image:tony.jpg|170px|left]]<br />
<br style="clear:both;" /><br />
'''Tony Bell''' <br /><br />
[[Tony Bell|home page]]<br />
<br />
[[Image:tim.jpg]] <br />
<br style="clear:both;" /><br />
'''Tim Blanche''' <br /><br />
[[Tim Blanche|home page]]<br />
<br />
[[Image:kilian.jpg|115px|left|link=Kilian Koepsell|Kilian Koepsell]]<br />
<br style="clear:both;" /><br />
'''Kilian Koepsell''' <br /><br />
[http://redwood.berkeley.edu/kilian/ home page] <br /><br />
[http://redwood.berkeley.edu/klab/ lab page]<br />
<br />
'''Mat Leonard''' <br /><br />
[[Mat Leonard|home page]]<br />
<br />
'''Joseph Thurakal''' <br /><br />
[[Joseph Thurakal|home page]]<br />
<br />
'''Sarah Marzen''' <br /><br />
[[Sarah Marzen|home page]]<br />
<br />
<br style="clear:both;" /><br />
'''Urs Koester''' <br /><br />
[[Urs | home page]]<br />
<br />
[[File:Gautam.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Gautam Agarwal''' <br /><br />
Postdoc at the Champalimaud Centre for the Unknown<br /><br />
[http://www.neuro.fchampalimaud.org/en/research/investigators/research-groups/group/Mainen/ home page]<br />
<br />
[[Image:ian.jpg|130px|left]]<br />
<br style="clear:both;" /><br />
'''Ian Stevenson''' <br /><br />
Currently at University of Connecticut<br /><br />
[http://homepages.uconn.edu/~ias13002/ home page]<br />
<br />
[[Image:paul_ivanov.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Paul Ivanov''' <br /><br />
[http://pirsquared.org/ home page]<br />
<br />
'''Daniel Little''' <br /><br />
[[Daniel Little|home page]]<br />
<br />
'''Chris Rodgers''' <br /><br />
[http://chris-rodgers.com/ home page]<br />
<br />
'''Sharif Corinaldi''' <br /><br />
[[Sharif Corinaldi|home page]]<br />
<br />
'''Amir Khosrowshahi''' <br /><br />
[[Amir Khosrowshahi|home page]]<br />
<br />
'''Antony Lee''' <br /><br />
[[Antony Lee|home page]]<br />
<br />
'''Chetan Nandakumar''' <br /><br />
[[Chetan Nandakumar|home page]]<br />
<br />
'''Jascha Sohl-Dickstein''' <br /><br />
[[Jascha Sohl-Dickstein|home page]]<br />
<br />
[[Image:jiminy.gif|left]]<br />
<br style="clear:both;" /><br />
'''Jimmy Wang''' <br /><br />
[https://redwood.berkeley.edu/jwang/index.html home page]<br />
<br />
'''Joel Zylberberg''' <br /><br />
[[Joel Zylberberg|home page]]<br />
<br />
'''Jack Culpepper''' <br /><br />
[http://www.cs.berkeley.edu/~bjc/ home page]<br />
<br />
'''Badr Faisal Albanna''' <br /><br />
[[Badr Faisal Albanna|home page]]<br />
<br />
'''Peter Battaglino''' <br /><br />
[[Peter Battaglino|home page]]<br />
<br />
[[Image:nicole.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Nicole Carlson''' <br /><br />
[[Nicole Carlson|home page]]<br />
<br />
'''Alfonso Apicella''' <br /><br />
[[Alfonso Apicella|home page]]<br />
<br />
[[Image:charles.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Charles Cadieu''' <br /><br />
[http://charles.cadieu.us home page]<br />
<br />
[[Image:NicolFace.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Nicol Harper''' <br /><br />
[[Nicol Harper|home page]]<br />
<br />
'''Vivek Ayer''' <br /><br />
[[Vivek Ayer|home page]]<br />
<br />
[[Image:matthias.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Matthias Bethge''', Postdoctoral Fellow, 2005 <br /><br />
Currently at the Max-Planck Institute, Tubingen <br /><br />
Winner of the Bernstein Independent Investigator Prize <br /><br />
[http://www.neuro.uni-bremen.de/~mbethge/ home page]<br />
<br />
'''Will Coulter''', MA Neuroscience (2009) <br /><br />
Currently at Siemens Healthcare Diagnostics R&D <br /><br />
[[Will Coulter|home page]] (outdated)<br />
<br />
'''Mohammad Dastjerdi''' <br /><br />
[[Mohammad Dastjerdi|home page]]<br />
<br />
[[Image:pierre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pierre Garrigues''' <br /><br />
[http://redwood.berkeley.edu/pierre home page]<br />
<br />
'''Joe Goldbeck''', MA Neuroscience (2012) <br /><br />
<br />
[[Image:thomas.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Thomas Lauritzen''', Research Scientist, 2005-2010 <br /><br />
Currently at Second Sight Medical Products, Sylmar, CA <br /><br />
[[Thomas Lauritzen|home page]]<br />
<br />
[[Image:Gianluca.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Gianluca Monaci''' <br /><br />
Currently at Philips Research, Eindhoven <br /><br />
[[Gianluca Monaci|home page]]<br />
<br />
'''Martin Rehn''' <br /><br />
[[Martin Rehn|home page]]<br />
<br />
[[Image:Rozell.jpg]]<br />
<br style="clear:both;" /><br />
'''Chris Rozell''' <br /><br />
[[http://www.ece.rice.edu/~crozell/ home page]]<br />
<br />
[[Image:Ivana.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Ivana Tosic''', Postdoctoral researcher, 2009-2011 <br /><br />
Currently with [http://rii.ricoh.com/ Ricoh Innovations Inc.], Menlo Park <br /><br />
[http://ivanatosic.net home page]</div>Dpaitonhttps://rctn.org/w/index.php?title=People_at_the_Redwood_Center&diff=8822People at the Redwood Center2017-07-05T19:51:22Z<p>Dpaiton: </p>
<hr />
<div>__NOEDITSECTION__<br />
== Faculty ==<br />
<br />
[[Image:deweese.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Michael R. DeWeese''', Associate Professor, HWNI and Physics <br /><br />
[http://physics.berkeley.edu/people/faculty/michael-deweese Physics home page] <br /><br />
[http://redwood.berkeley.edu/wiki/Michael_DeWeese publications]<br />
<br />
[[Image:bruno.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Bruno Olshausen''', Director and Professor, HWNI and School of Optometry <br /><br />
[http://redwood.berkeley.edu/bruno home page]<br />
<br />
<br />
[[Image:fritz.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Fritz Sommer''', Acting Director and Associate Adjunct Professor, HWNI <br /><br />
[[Fritz Sommer|home page]]<br />
<br />
== Research Scientists ==<br />
<br style="clear:both;" /><br />
'''Pentti Kanerva''' <br /><br />
[[Pentti Kanerva|home page]]<br />
<br />
'''Vivienne L'Ecuyer Ming''', Visiting Scholar<br /><br />
[http://www.vivienneming.com home page]<br />
<br />
[[Image:bnfcjh.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Chris Hillar''' <br /><br />
[http://www.msri.org/people/members/chillar/ home page]<br />
<br />
<br />
[[Image:Allie_photo.jpeg]]<br />
<br style="clear:both;" /><br />
'''Alyson "Allie" Fletcher''' <br/><br />
Affiliated Researcher & UCSC Professor <br/><br />
[http://users.soe.ucsc.edu/~afletcher/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Karl Zipser''' <br/><br />
Assistant Researcher<br/><br />
[http://karlzipser.com home page]<br />
<br />
[[Image:Mika_photo.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
<b>Mika Laiho</b> <br /><br />
Visiting Scholar <br /><br />
[http://trc.utu.fi/microelectronics/research/emerging-computing-schemes Website]<br />
<br />
== Postdocs ==<br />
'''Petr Ježek''' <br /><br />
[[Petr Jezek|home page]]<br />
<br />
== Staff ==<br />
[[Image:JeffTeeters.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Jeff teeters''' <br /><br />
[[Jeff Teeters|home page]]<br />
<br />
== Students ==<br />
<br />
[[File:Neha_profile.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Neha Wadia''' <br />
[http://redwood.berkeley.edu/w/images/0/01/Neha_resume.pdf CV]<br />
<br />
[[File:Alex.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Alex Anderson''' <br /><br />
[http://www.ocf.berkeley.edu/~aga/ Website]<br />
<br />
[[File:JamesA.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''James Arnemann''' <br /><br />
[[James Arnemann|home page]]<br />
<br />
'''Brian Cheung''' <br /><br />
[[Brian Cheung|home page]]<br />
<br />
'''Dylan Paiton''' <br /><br />
[http://vision.berkeley.edu/?p=3052 home page]<br />
<br />
[[File:yubei.png|150px|left]]<br />
<br style="clear:both;" /><br />
'''Yubei Chen''' <br /><br />
[[Yubei Chen|home page]]<br />
<br />
'''Guy Isely''' <br /><br />
[http://gisely.github.io/ home page]<br />
<br />
[[File:spencer_redwood_people.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Spencer Kent''' <br /><br />
[http://www.spencerkent.com home page]<br />
<br />
[[File:Shariq222.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Shariq Mobin''' <br /><br />
[[Shariq Mobin|home page]]<br />
<br />
[[Image:mayur_mudigonda.jpg|alt=Mayur: the Magical Machine Learning Pony!|left]]<br />
<br style="clear:both;" /><br />
'''Mayur Mudigonda''' <br /><br />
[[http://redwood.berkeley.edu/mayur home page]]<br />
<br />
[[File:ogre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Chris Warner''' <br /><br />
[https://redwood.berkeley.edu/wiki/User:Cwarner home page]<br />
<br />
[[File:eric.JPG|150px|left]]<br />
<br style="clear:both;" /><br />
'''Eric Weiss''' <br /><br />
[[Eric Weiss|home page]]<br />
<br />
[[File:Pooneh.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pooneh Mohammadiara''' <br/><br />
[[Pooneh Mohammadiara| home page]]<br />
<br />
[[File:Vasha.jpeg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Vasha DuTell''' <br/><br />
[[Vasha DuTell| home page]]<br />
<br />
== Alumni ==<br />
<br />
'''Jesse Livezey''' <br /><br />
[http://jesselivezey.com/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Alex Bujan''' <br /><br />
[http://afbujan.github.io/ Website]<br />
<br />
'''Chayut Thanapirom''' <br /><br />
[[Chayut Thanapirom|home page]]<br />
<br />
[[Image:tony.jpg|170px|left]]<br />
<br style="clear:both;" /><br />
'''Tony Bell''' <br /><br />
[[Tony Bell|home page]]<br />
<br />
[[Image:tim.jpg]] <br />
<br style="clear:both;" /><br />
'''Tim Blanche''' <br /><br />
[[Tim Blanche|home page]]<br />
<br />
[[Image:kilian.jpg|115px|left|link=Kilian Koepsell|Kilian Koepsell]]<br />
<br style="clear:both;" /><br />
'''Kilian Koepsell''' <br /><br />
[http://redwood.berkeley.edu/kilian/ home page] <br /><br />
[http://redwood.berkeley.edu/klab/ lab page]<br />
<br />
'''Mat Leonard''' <br /><br />
[[Mat Leonard|home page]]<br />
<br />
'''Joseph Thurakal''' <br /><br />
[[Joseph Thurakal|home page]]<br />
<br />
'''Sarah Marzen''' <br /><br />
[[Sarah Marzen|home page]]<br />
<br />
<br style="clear:both;" /><br />
'''Urs Koester''' <br /><br />
[[Urs | home page]]<br />
<br />
[[File:Gautam.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Gautam Agarwal''' <br /><br />
Postdoc at the Champalimaud Centre for the Unknown<br /><br />
[http://www.neuro.fchampalimaud.org/en/research/investigators/research-groups/group/Mainen/ home page]<br />
<br />
[[Image:ian.jpg|130px|left]]<br />
<br style="clear:both;" /><br />
'''Ian Stevenson''' <br /><br />
Currently at University of Connecticut<br /><br />
[http://homepages.uconn.edu/~ias13002/ home page]<br />
<br />
[[Image:paul_ivanov.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Paul Ivanov''' <br /><br />
[http://pirsquared.org/ home page]<br />
<br />
'''Daniel Little''' <br /><br />
[[Daniel Little|home page]]<br />
<br />
'''Chris Rodgers''' <br /><br />
[http://chris-rodgers.com/ home page]<br />
<br />
'''Sharif Corinaldi''' <br /><br />
[[Sharif Corinaldi|home page]]<br />
<br />
'''Amir Khosrowshahi''' <br /><br />
[[Amir Khosrowshahi|home page]]<br />
<br />
'''Antony Lee''' <br /><br />
[[Antony Lee|home page]]<br />
<br />
'''Chetan Nandakumar''' <br /><br />
[[Chetan Nandakumar|home page]]<br />
<br />
'''Jascha Sohl-Dickstein''' <br /><br />
[[Jascha Sohl-Dickstein|home page]]<br />
<br />
[[Image:jiminy.gif|left]]<br />
<br style="clear:both;" /><br />
'''Jimmy Wang''' <br /><br />
[https://redwood.berkeley.edu/jwang/index.html home page]<br />
<br />
'''Joel Zylberberg''' <br /><br />
[[Joel Zylberberg|home page]]<br />
<br />
'''Jack Culpepper''' <br /><br />
[http://www.cs.berkeley.edu/~bjc/ home page]<br />
<br />
'''Badr Faisal Albanna''' <br /><br />
[[Badr Faisal Albanna|home page]]<br />
<br />
'''Peter Battaglino''' <br /><br />
[[Peter Battaglino|home page]]<br />
<br />
[[Image:nicole.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Nicole Carlson''' <br /><br />
[[Nicole Carlson|home page]]<br />
<br />
'''Alfonso Apicella''' <br /><br />
[[Alfonso Apicella|home page]]<br />
<br />
[[Image:charles.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Charles Cadieu''' <br /><br />
[http://charles.cadieu.us home page]<br />
<br />
[[Image:NicolFace.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Nicol Harper''' <br /><br />
[[Nicol Harper|home page]]<br />
<br />
'''Vivek Ayer''' <br /><br />
[[Vivek Ayer|home page]]<br />
<br />
[[Image:matthias.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Matthias Bethge''', Postdoctoral Fellow, 2005 <br /><br />
Currently at the Max-Planck Institute, Tubingen <br /><br />
Winner of the Bernstein Independent Investigator Prize <br /><br />
[http://www.neuro.uni-bremen.de/~mbethge/ home page]<br />
<br />
'''Will Coulter''', MA Neuroscience (2009) <br /><br />
Currently at Siemens Healthcare Diagnostics R&D <br /><br />
[[Will Coulter|home page]] (outdated)<br />
<br />
'''Mohammad Dastjerdi''' <br /><br />
[[Mohammad Dastjerdi|home page]]<br />
<br />
[[Image:pierre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pierre Garrigues''' <br /><br />
[http://redwood.berkeley.edu/pierre home page]<br />
<br />
'''Joe Goldbeck''', MA Neuroscience (2012) <br /><br />
<br />
[[Image:thomas.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Thomas Lauritzen''', Research Scientist, 2005-2010 <br /><br />
Currently at Second Sight Medical Products, Sylmar, CA <br /><br />
[[Thomas Lauritzen|home page]]<br />
<br />
[[Image:Gianluca.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Gianluca Monaci''' <br /><br />
Currently at Philips Research, Eindhoven <br /><br />
[[Gianluca Monaci|home page]]<br />
<br />
'''Martin Rehn''' <br /><br />
[[Martin Rehn|home page]]<br />
<br />
[[Image:Rozell.jpg]]<br />
<br style="clear:both;" /><br />
'''Chris Rozell''' <br /><br />
[[http://www.ece.rice.edu/~crozell/ home page]]<br />
<br />
[[Image:Ivana.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Ivana Tosic''', Postdoctoral researcher, 2009-2011 <br /><br />
Currently with [http://rii.ricoh.com/ Ricoh Innovations Inc.], Menlo Park <br /><br />
[http://ivanatosic.net home page]</div>Dpaitonhttps://rctn.org/w/index.php?title=People_at_the_Redwood_Center&diff=8821People at the Redwood Center2017-07-05T19:50:18Z<p>Dpaiton: </p>
<hr />
<div>__NOEDITSECTION__<br />
== Faculty ==<br />
<br />
[[Image:deweese.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Michael R. DeWeese''', Associate Professor, HWNI and Physics <br /><br />
[http://physics.berkeley.edu/people/faculty/michael-deweese Physics home page] <br /><br />
[http://redwood.berkeley.edu/wiki/Michael_DeWeese publications]<br />
<br />
[[Image:bruno.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Bruno Olshausen''', Director and Professor, HWNI and School of Optometry <br /><br />
[http://redwood.berkeley.edu/bruno home page]<br />
<br />
<br />
[[Image:fritz.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Fritz Sommer''', Acting Director and Associate Adjunct Professor, HWNI <br /><br />
[[Fritz Sommer|home page]]<br />
<br />
<br />
== Research Scientists ==<br />
<br style="clear:both;" /><br />
'''Pentti Kanerva''' <br /><br />
[[Pentti Kanerva|home page]]<br />
<br />
'''Vivienne L'Ecuyer Ming''', Visiting Scholar<br /><br />
[http://www.vivienneming.com home page]<br />
<br />
[[Image:bnfcjh.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Chris Hillar''' <br /><br />
[http://www.msri.org/people/members/chillar/ home page]<br />
<br />
<br />
[[Image:Allie_photo.jpeg]]<br />
<br style="clear:both;" /><br />
'''Alyson "Allie" Fletcher''' <br/><br />
Affiliated Researcher & UCSC Professor <br/><br />
[http://users.soe.ucsc.edu/~afletcher/ home page]<br />
<br />
<br style="clear:both;" /><br />
'''Karl Zipser''' <br/><br />
Assistant Researcher<br/><br />
[http://karlzipser.com home page]<br />
<br />
[[Image:Mika_photo.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
<b>Mika Laiho</b> <br /><br />
Visiting Scholar <br /><br />
[http://trc.utu.fi/microelectronics/research/emerging-computing-schemes Website]<br />
<br />
== Postdocs ==<br />
'''Petr Ježek''' <br /><br />
[[Petr Jezek|home page]]<br />
<br />
== Staff ==<br />
[[Image:JeffTeeters.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Jeff teeters''' <br /><br />
[[Jeff Teeters|home page]]<br />
<br />
== Students ==<br />
<br />
[[File:Neha_profile.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Neha Wadia''' <br />
[http://redwood.berkeley.edu/w/images/0/01/Neha_resume.pdf CV]<br />
<br />
[[File:Alex.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Alex Anderson''' <br /><br />
[http://www.ocf.berkeley.edu/~aga/ Website]<br />
<br />
[[File:JamesA.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''James Arnemann''' <br /><br />
[[James Arnemann|home page]]<br />
<br />
'''Brian Cheung''' <br /><br />
[[Brian Cheung|home page]]<br />
<br />
'''Dylan Paiton''' <br /><br />
[http://vision.berkeley.edu/?p=3052 home page]<br />
<br />
[[File:yubei.png|150px|left]]<br />
<br style="clear:both;" /><br />
'''Yubei Chen''' <br /><br />
[[Yubei Chen|home page]]<br />
<br />
'''Guy Isely''' <br /><br />
[http://gisely.github.io/ home page]<br />
<br />
[[File:spencer_redwood_people.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Spencer Kent''' <br /><br />
[http://www.spencerkent.com home page]<br />
<br />
'''Jesse Livezey''' <br /><br />
[http://jesselivezey.com/ home page]<br />
<br />
[[File:Shariq222.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Shariq Mobin''' <br /><br />
[[Shariq Mobin|home page]]<br />
<br />
[[Image:mayur_mudigonda.jpg|alt=Mayur: the Magical Machine Learning Pony!|left]]<br />
<br style="clear:both;" /><br />
'''Mayur Mudigonda''' <br /><br />
[[http://redwood.berkeley.edu/mayur home page]]<br />
<br />
'''Chayut Thanapirom''' <br /><br />
[[Chayut Thanapirom|home page]]<br />
<br />
[[File:ogre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Chris Warner''' <br /><br />
[https://redwood.berkeley.edu/wiki/User:Cwarner home page]<br />
<br />
[[File:eric.JPG|150px|left]]<br />
<br style="clear:both;" /><br />
'''Eric Weiss''' <br /><br />
[[Eric Weiss|home page]]<br />
<br />
[[File:Pooneh.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pooneh Mohammadiara''' <br/><br />
[[Pooneh Mohammadiara| home page]]<br />
<br />
[[File:Vasha.jpeg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Vasha DuTell''' <br/><br />
[[Vasha DuTell| home page]]<br />
<br />
== Alumni ==<br />
<br style="clear:both;" /><br />
'''Alex Bujan''' <br /><br />
[http://afbujan.github.io/ Website]<br />
<br />
[[Image:tony.jpg|170px|left]]<br />
<br style="clear:both;" /><br />
'''Tony Bell''' <br /><br />
[[Tony Bell|home page]]<br />
<br />
[[Image:tim.jpg]] <br />
<br style="clear:both;" /><br />
'''Tim Blanche''' <br /><br />
[[Tim Blanche|home page]]<br />
<br />
[[Image:kilian.jpg|115px|left|link=Kilian Koepsell|Kilian Koepsell]]<br />
<br style="clear:both;" /><br />
'''Kilian Koepsell''' <br /><br />
[http://redwood.berkeley.edu/kilian/ home page] <br /><br />
[http://redwood.berkeley.edu/klab/ lab page]<br />
<br />
'''Mat Leonard''' <br /><br />
[[Mat Leonard|home page]]<br />
<br />
'''Joseph Thurakal''' <br /><br />
[[Joseph Thurakal|home page]]<br />
<br />
'''Sarah Marzen''' <br /><br />
[[Sarah Marzen|home page]]<br />
<br />
<br style="clear:both;" /><br />
'''Urs Koester''' <br /><br />
[[Urs | home page]]<br />
<br />
[[File:Gautam.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Gautam Agarwal''' <br /><br />
Postdoc at the Champalimaud Centre for the Unknown<br /><br />
[http://www.neuro.fchampalimaud.org/en/research/investigators/research-groups/group/Mainen/ home page]<br />
<br />
[[Image:ian.jpg|130px|left]]<br />
<br style="clear:both;" /><br />
'''Ian Stevenson''' <br /><br />
Currently at University of Connecticut<br /><br />
[http://homepages.uconn.edu/~ias13002/ home page]<br />
<br />
[[Image:paul_ivanov.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Paul Ivanov''' <br /><br />
[http://pirsquared.org/ home page]<br />
<br />
'''Daniel Little''' <br /><br />
[[Daniel Little|home page]]<br />
<br />
'''Chris Rodgers''' <br /><br />
[http://chris-rodgers.com/ home page]<br />
<br />
'''Sharif Corinaldi''' <br /><br />
[[Sharif Corinaldi|home page]]<br />
<br />
'''Amir Khosrowshahi''' <br /><br />
[[Amir Khosrowshahi|home page]]<br />
<br />
'''Antony Lee''' <br /><br />
[[Antony Lee|home page]]<br />
<br />
'''Chetan Nandakumar''' <br /><br />
[[Chetan Nandakumar|home page]]<br />
<br />
'''Jascha Sohl-Dickstein''' <br /><br />
[[Jascha Sohl-Dickstein|home page]]<br />
<br />
[[Image:jiminy.gif|left]]<br />
<br style="clear:both;" /><br />
'''Jimmy Wang''' <br /><br />
[https://redwood.berkeley.edu/jwang/index.html home page]<br />
<br />
'''Joel Zylberberg''' <br /><br />
[[Joel Zylberberg|home page]]<br />
<br />
'''Jack Culpepper''' <br /><br />
[http://www.cs.berkeley.edu/~bjc/ home page]<br />
<br />
'''Badr Faisal Albanna''' <br /><br />
[[Badr Faisal Albanna|home page]]<br />
<br />
'''Peter Battaglino''' <br /><br />
[[Peter Battaglino|home page]]<br />
<br />
[[Image:nicole.jpg|left]]<br />
<br style="clear:both;" /><br />
'''Nicole Carlson''' <br /><br />
[[Nicole Carlson|home page]]<br />
<br />
'''Alfonso Apicella''' <br /><br />
[[Alfonso Apicella|home page]]<br />
<br />
[[Image:charles.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Charles Cadieu''' <br /><br />
[http://charles.cadieu.us home page]<br />
<br />
[[Image:NicolFace.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Nicol Harper''' <br /><br />
[[Nicol Harper|home page]]<br />
<br />
'''Vivek Ayer''' <br /><br />
[[Vivek Ayer|home page]]<br />
<br />
[[Image:matthias.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Matthias Bethge''', Postdoctoral Fellow, 2005 <br /><br />
Currently at the Max-Planck Institute, Tubingen <br /><br />
Winner of the Bernstein Independent Investigator Prize <br /><br />
[http://www.neuro.uni-bremen.de/~mbethge/ home page]<br />
<br />
'''Will Coulter''', MA Neuroscience (2009) <br /><br />
Currently at Siemens Healthcare Diagnostics R&D <br /><br />
[[Will Coulter|home page]] (outdated)<br />
<br />
'''Mohammad Dastjerdi''' <br /><br />
[[Mohammad Dastjerdi|home page]]<br />
<br />
[[Image:pierre.jpg|150px|left]]<br />
<br style="clear:both;" /><br />
'''Pierre Garrigues''' <br /><br />
[http://redwood.berkeley.edu/pierre home page]<br />
<br />
'''Joe Goldbeck''', MA Neuroscience (2012) <br /><br />
<br />
[[Image:thomas.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Thomas Lauritzen''', Research Scientist, 2005-2010 <br /><br />
Currently at Second Sight Medical Products, Sylmar, CA <br /><br />
[[Thomas Lauritzen|home page]]<br />
<br />
[[Image:Gianluca.jpg|115px|left]]<br />
<br style="clear:both;" /><br />
'''Gianluca Monaci''' <br /><br />
Currently at Philips Research, Eindhoven <br /><br />
[[Gianluca Monaci|home page]]<br />
<br />
'''Martin Rehn''' <br /><br />
[[Martin Rehn|home page]]<br />
<br />
[[Image:Rozell.jpg]]<br />
<br style="clear:both;" /><br />
'''Chris Rozell''' <br /><br />
[[http://www.ece.rice.edu/~crozell/ home page]]<br />
<br />
[[Image:Ivana.jpg|left]] <br />
<br style="clear:both;" /><br />
'''Ivana Tosic''', Postdoctoral researcher, 2009-2011 <br /><br />
Currently with [http://rii.ricoh.com/ Ricoh Innovations Inc.], Menlo Park <br /><br />
[http://ivanatosic.net home page]</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8725Seminars2017-03-08T20:14:12Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
'''March 1st, 2017'''<br />
* Speaker: Sahar Akram<br />
* Time: 12:00<br />
* Affiliation: Starkey Hearing Research Center <br />
* Host: Shariq<br />
* Status: Confirmed<br />
* Title: Real-Time & Adaptive Auditory Neural Processing<br />
* Abstract: Decoding the dynamics of brain activity underlying conscious behavior is one of the key questions in systems neuroscience. Sensory neurons, such as those in the auditory system, can undergo rapid and task-dependent changes in their response characteristics during attentive behavior, and thereby result in functional changes in the system over time. In order to quantify human’s conscious experience, neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) are widely used to record the neural activity from the brain with millisecond temporal resolution. Therefore, a dynamic decoding framework on par with the sampling resolution of EEG/MEG is crucial in order to better understand the neural correlates underlying sophisticated cognitive functions such as attention. I will talk about two recent attempts on real-time decoding of brain neural activity during a competing auditory attention task, using Bayesian hierarchical modeling and adaptive signal processing.<br />
<br />
'''Mar 2, 2017'''<br />
* Speaker: Joszef Fiser<br />
* Time: 12:00<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Mar 22, 2017'''<br />
* Speaker: Michael Frank<br />
* Time: 12:00<br />
* Affiliation: Magicore Systems<br />
* Host: Dylan<br />
* Status: Confirmed<br />
* Title: The Future of the Multi-core Platform Task-Superscalar Extensions to Von-Neumann Architecture and Optimization for Neural Networks<br />
* Abstract: Technology scaling had been carrying computer science thru the second half of the 20th century until single CPU performance started leveling off, after which multi- and many-core processors, including GPUs, emerged as the substrate for high performance computing. Mobile market implementations followed this trend and today you might be carrying a phone with more than 16 different processors. For power efficiency reasons, many of the cores are specialized to perform limited functions (such as modem or connectivity control, graphics rendering, or future neural-network acceleration) with most mainstream phones containing four or more general purpose processors. As Steve Jobs insightfully commented almost a decade ago, “The way the processor industry is going is to add more and more cores, but nobody knows how to program those things.” Jobs was correct, programming these multiprocessor systems has become a challenge and several programming models have been proposed in academia to address this issue. Power and thermals are also an ever present thorn to mass market applications. Through the years, CPUs based on the von-Neumann architecture have fended off attacks from many directions; today complex super-scalar implementations execute multiple instructions each clock cycle, parallel and out-of-order, keeping up the illusion of sequential processing. Recent research demonstrates though that augmenting the paradigm of the Von-Neumann architecture with a few established concepts from data-flow and task-parallel programming, will create both a credible and intuitive parallel architecture enabling notable compute efficiency improvement while retaining compatibility with the current mainstream. This talk will thus review the current state of the processor industry and after highlighting why we are running out of steam in ILP; I will outline the task-superscalar programming model as the “ring to rule them all” and provide insights as to how this architecture can take advantage of special HW acceleration for data-flow management and provide support for efficient neuromorphic computing.<br />
<br />
'''April 12, 2017'''<br />
* Speaker: Aapo Hyvarinen<br />
* Time: 12:00<br />
* Affiliation: Gatsby/UCL<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''TBD, 2016'''<br />
* Speaker: Alexander Stubbs<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Michael Levy<br />
* Status: tentative<br />
* Title: Could chromatic aberration allow for an alternative evolutionary pathway towards color vision?<br />
* Abstract: We present a mechanism by which organisms with only a single photoreceptor, which have a monochromatic view of the world, can achieve color discrimination. An off-axis pupil and the principle of chromatic aberration (where different wavelengths come to focus at different distances behind a lens) can combine to provide “color-blind” animals with a way to distinguish colors. As a specific example, we constructed a computer model of the visual system of cephalopods (octopus, squid, and cuttlefish) that have a single unfiltered photoreceptor type. We compute a quantitative image quality budget for this visual system and show how chromatic blurring dominates the visual acuity in these animals in shallow water. This proposed mechanism is consistent with the extensive suite of visual/behavioral and physiological data that has been obtained from cephalopod studies and offers a possible solution to the apparent paradox of vivid chromatic behaviors in color blind animals. Moreover, this proposed mechanism has potential applicability in organisms with limited photoreceptor complements, such as spiders and dolphins.<br />
<br />
'''Sept. 4 or 11, 2017'''<br />
* Speaker: Carl Pabo<br />
* Time: 12:00<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2016/17 academic year ===<br />
<br />
'''Sept. 7, 2016'''<br />
* Speaker: Dan Stowell<br />
* Time: 12:00<br />
* Affiliation: Queen Mary, University of London<br />
* Host: Frederic Theunissen<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 8, 2016'''<br />
* Speaker: Barb Finlay<br />
* Time: 12:00<br />
* Affiliation: Cornell Univ<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 27, 2016'''<br />
* Speaker: Yoshua Bengio<br />
* Time: 11:00<br />
* Affiliation: Univ Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Oct. 12, 2016'''<br />
* Speaker: Paul Rhodes<br />
* Time: 4:00<br />
* Affiliation: Specific Technologies<br />
* Host: Dylan/Bruno<br />
* Status: confirmed<br />
* Title: A novel and important problem in spatiotemporal pattern classification<br />
* Abstract: Specific Technologies uses a sensor response that consists of a vector time series, a spatiotemporal fingerprint, to classify bacteria at the strain level during their growth. The identification of resistant strains of bacteria has become one of the world's great problems (here is a link to a $20M prize that the US govt has issued: https://www.nih.gov/news-events/news-releases/federal-prize-competition-seeks-innovative-ideas-combat-antimicrobial-resistance). We are using deep convolutional nets to do this classification, but they are instantaneous, and so do not capture the temporal patterns that are often at the core of what differentiates strains. So using the full temporal character of the sensor response time series is a cutting edge neural ML problem, and important to society too.<br />
<br />
'''Oct. 25, 2016'''<br />
* Speaker: Douglas L. Jones<br />
* Time: 2:00<br />
* Affiliation: ECE Department, University of Illinois at Urbana-Champaign<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Optimal energy-efficient coding in sensory neurons<br />
* Abstract: Evolutionary pressure suggests that the spike-based code in the sensory nervous system should satisfy two opposing constraints: 1) minimize signal distortion in the encoding process (i.e., maintain fidelity) by keeping the average spike rate as high as possible, and 2) minimize the metabolic load on the neuron by keeping the average spike rate as low as possible. We hypothesize that selective pressure has shaped the biophysics of a neuron to satisfy these conflicting demands. An energy-fidelity trade-off can be obtained through a constrained optimization process that achieves the lowest signal distortion for a given constraint on the spike rate. We derive the asymptotically optimal average-energy-constrained neuronal source code and show that it leads to a dynamic threshold that functions as an internal decoder (reconstruction filter) and adapts a spike-firing threshold so that spikes are emitted only when the coding error reaches this threshold. A stochastic extension is obtained by adding internal noise (dithering, or stochastic resonance) to the spiking threshold. We show that the source-coding neuron model i) reproduces experimentally observed spike-times in response to a stimulus, and ii) reproduces the serial correlations in the observed sequence of inter-spike intervals, using data from a peripheral sensory neuron and a central (cortical) somatosensory neuron. Finally, we show that the spike-timing code, although a temporal code, is in the limit of high firing rates an instantaneous rate code and accurately predicts the peri-stimulus time histogram (PSTH). We conclude by suggesting possible biophysical (ionic) mechanisms for this coding scheme.<br />
<br />
'''October 26, 2016'''<br />
* Speaker: Eric Jonas<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Charles Frye<br />
* Status: confirmed<br />
* Title: Could a neuroscientist understand a microprocessor?<br />
* Abstract: There is a popular belief in neuroscience that we are primarily data limited, that producing large, multimodal, and complex datasets will, enabled by data analysis algorithms, lead to fundamental insights into the way the brain processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. Here we take a simulated classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the processor. This suggests that current computational approaches in neuroscience may fall short of producing meaningful models of the brain. We discuss several obvious shortcomings with this model, and ways that they might be addressed, both experimentally and computationally. <br />
* Bio: Eric Jonas is currently a postdoc in computer science at UC Berkeley working with Ben Recht on machine learning for scientific data acquisition. He earned his PhD in Computational Neuroscience, M. Eng in Electrical Engineering, BS in Electrical Engineering and Computer Science, and BS in Neurobiology, all from MIT. Prior to his return to academia, he was founder and CEO of Prior Knowledge, a predictive database company which was acquired in 2012 by Salesforce.com, where he was Chief Predictive Scientist until 2014. In 2015 he was named one of the top rising stars in bioengineering by the Defense Department’s Advanced Research Projects Agency (DARPA).<br />
<br />
'''Nov. 9, 2016'''<br />
* Speaker: Pulkit Agrawal<br />
* Time: 12:00<br />
* Affiliation: EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''Nov. 16, 2016'''<br />
* Speaker: Sebastian Musslick<br />
* Time: 12:00<br />
* Affiliation: Princeton Neuroscience Institute (Princeton University)<br />
* Host: Brian Cheung<br />
* Status: confirmed<br />
* Title: Parallel Processing Capability Versus Efficiency of Representation in Neural Network Architectures<br />
* Abstract: One of the most salient and well-recognized features of human goal-directed behavior is our limited ability to conduct multiple demanding tasks at once. Why is this? Some have suggested it reflects metabolic limitations, or structural ones. However, both explanations are unlikely. The brain routinely demonstrates the ability to carry out a multitude of processes in an enduring and parallel manner (walking, breathing, listening). Why, in contrast, is its capacity for allocating attention to control-demanding tasks - such a critical and powerful function - so limited? In the first part of my talk I will describe a computational framework that explains limitations of parallel processing in neural network architectures as the result of cross-talk between shared task representations. Using graph-theoretic analyses we show that the parallel processing (multitasking) capability of two-layer networks drops precipitously as a function of task pathway overlap, and scales highly sublinearly with network size. I will describe how this analysis can be applied to task representations encoded in neural networks or neuroimaging data, and show how it can be used to predict both concurrent and sequential multitasking performance in trained neural networks based on single task representations. Our results suggest that maximal parallel processing performance is achieved by segregating task pathways, by separating the representations on which they rely. However, there is a countervailing pressure for pathways to intersect: the re-use of representations to facilitate learning of new tasks. In the second part of my talk I will demonstrate a tradeoff between learning efficiency and parallel processing capability in neural networks. It can be shown that weight priors on learned task similarity improve learning speed and generalization but lead to strong constraints on parallel processing capability. These findings will be contrasted with an ongoing behavioral study by assessing learning and multitasking performance of human subjects across tasks with varying degrees of feature-overlap.<br />
<br />
'''Nov 30, 2016'''<br />
* Speaker: Marcus Rohrbach<br />
* Time: 12:00<br />
* Affiliation: EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title: Sparse Coding ASIC Chips for Feature Extraction and Classification<br />
* Abstract: Hardware-based computer vision accelerators will be an essential part of future mobile and autonomous devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to the underlying algorithms, which is an advantage over software-based solutions and general-purpose hardware. In this talk, I will present three application-specific integrated circuit (ASIC) chips that implement the sparse and independent local network (SAILnet) algorithm and the locally competitive algorithm (LCA) for feature extraction and classification. Two of the chips were designed using an array of leaky integrate-and-fire neurons. Sparse activations of the neurons make possible an efficient grid-ring architecture to deliver an image processing throughput of 1 G pixel/s using only 200 mW. The third chip was designed using a convolution approach. Sparsity is again an important factor that enabled the use of sparse convolvers to achieve an effective performance of 900 G operations/s using less than 150 mW.<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
'''June 8, 2016'''<br />
* Speaker: Kris Bouchard<br />
* Time: 12:00<br />
* Affiliation: LBNL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: The union of intersections method<br />
* Abstract:<br />
<br />
'''June 15, 2016'''<br />
* Speaker: James Blackmon<br />
* Time: 12:00<br />
* Affiliation: San Francisco State University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8724Seminars2017-03-06T23:54:18Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
'''March 1st, 2017'''<br />
* Speaker: Sahar Akram<br />
* Time: 12:00<br />
* Affiliation: Starkey Hearing Research Center <br />
* Host: Shariq<br />
* Status: Confirmed<br />
* Title: Real-Time & Adaptive Auditory Neural Processing<br />
* Abstract: Decoding the dynamics of brain activity underlying conscious behavior is one of the key questions in systems neuroscience. Sensory neurons, such as those in the auditory system, can undergo rapid and task-dependent changes in their response characteristics during attentive behavior, and thereby result in functional changes in the system over time. In order to quantify human’s conscious experience, neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) are widely used to record the neural activity from the brain with millisecond temporal resolution. Therefore, a dynamic decoding framework on par with the sampling resolution of EEG/MEG is crucial in order to better understand the neural correlates underlying sophisticated cognitive functions such as attention. I will talk about two recent attempts on real-time decoding of brain neural activity during a competing auditory attention task, using Bayesian hierarchical modeling and adaptive signal processing.<br />
<br />
'''Mar 2, 2017'''<br />
* Speaker: Joszef Fiser<br />
* Time: 12:00<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Mar 22, 2017'''<br />
* Speaker: Michael Frank<br />
* Time: 12:00<br />
* Affiliation: Magicore Systems<br />
* Host: Dylan<br />
* Status: Tentative date - unconfirmed<br />
* Title: The Future of the Multi-core Platform Task-Superscalar Extensions to Von-Neumann Architecture and Optimization for Neural Networks<br />
* Abstract: Technology scaling had been carrying computer science thru the second half of the 20th century until single CPU performance started leveling off, after which multi- and many-core processors, including GPUs, emerged as the substrate for high performance computing. Mobile market implementations followed this trend and today you might be carrying a phone with more than 16 different processors. For power efficiency reasons, many of the cores are specialized to perform limited functions (such as modem or connectivity control, graphics rendering, or future neural-network acceleration) with most mainstream phones containing four or more general purpose processors. As Steve Jobs insightfully commented almost a decade ago, “The way the processor industry is going is to add more and more cores, but nobody knows how to program those things.” Jobs was correct, programming these multiprocessor systems has become a challenge and several programming models have been proposed in academia to address this issue. Power and thermals are also an ever present thorn to mass market applications. Through the years, CPUs based on the von-Neumann architecture have fended off attacks from many directions; today complex super-scalar implementations execute multiple instructions each clock cycle, parallel and out-of-order, keeping up the illusion of sequential processing. Recent research demonstrates though that augmenting the paradigm of the Von-Neumann architecture with a few established concepts from data-flow and task-parallel programming, will create both a credible and intuitive parallel architecture enabling notable compute efficiency improvement while retaining compatibility with the current mainstream. This talk will thus review the current state of the processor industry and after highlighting why we are running out of steam in ILP; I will outline the task-superscalar programming model as the “ring to rule them all” and provide insights as to how this architecture can take advantage of special HW acceleration for data-flow management and provide support for efficient neuromorphic computing.<br />
<br />
'''April 12, 2017'''<br />
* Speaker: Aapo Hyvarinen<br />
* Time: 12:00<br />
* Affiliation: Gatsby/UCL<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''TBD, 2016'''<br />
* Speaker: Alexander Stubbs<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Michael Levy<br />
* Status: tentative<br />
* Title: Could chromatic aberration allow for an alternative evolutionary pathway towards color vision?<br />
* Abstract: We present a mechanism by which organisms with only a single photoreceptor, which have a monochromatic view of the world, can achieve color discrimination. An off-axis pupil and the principle of chromatic aberration (where different wavelengths come to focus at different distances behind a lens) can combine to provide “color-blind” animals with a way to distinguish colors. As a specific example, we constructed a computer model of the visual system of cephalopods (octopus, squid, and cuttlefish) that have a single unfiltered photoreceptor type. We compute a quantitative image quality budget for this visual system and show how chromatic blurring dominates the visual acuity in these animals in shallow water. This proposed mechanism is consistent with the extensive suite of visual/behavioral and physiological data that has been obtained from cephalopod studies and offers a possible solution to the apparent paradox of vivid chromatic behaviors in color blind animals. Moreover, this proposed mechanism has potential applicability in organisms with limited photoreceptor complements, such as spiders and dolphins.<br />
<br />
'''Sept. 4 or 11, 2017'''<br />
* Speaker: Carl Pabo<br />
* Time: 12:00<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2016/17 academic year ===<br />
<br />
'''Sept. 7, 2016'''<br />
* Speaker: Dan Stowell<br />
* Time: 12:00<br />
* Affiliation: Queen Mary, University of London<br />
* Host: Frederic Theunissen<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 8, 2016'''<br />
* Speaker: Barb Finlay<br />
* Time: 12:00<br />
* Affiliation: Cornell Univ<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 27, 2016'''<br />
* Speaker: Yoshua Bengio<br />
* Time: 11:00<br />
* Affiliation: Univ Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Oct. 12, 2016'''<br />
* Speaker: Paul Rhodes<br />
* Time: 4:00<br />
* Affiliation: Specific Technologies<br />
* Host: Dylan/Bruno<br />
* Status: confirmed<br />
* Title: A novel and important problem in spatiotemporal pattern classification<br />
* Abstract: Specific Technologies uses a sensor response that consists of a vector time series, a spatiotemporal fingerprint, to classify bacteria at the strain level during their growth. The identification of resistant strains of bacteria has become one of the world's great problems (here is a link to a $20M prize that the US govt has issued: https://www.nih.gov/news-events/news-releases/federal-prize-competition-seeks-innovative-ideas-combat-antimicrobial-resistance). We are using deep convolutional nets to do this classification, but they are instantaneous, and so do not capture the temporal patterns that are often at the core of what differentiates strains. So using the full temporal character of the sensor response time series is a cutting edge neural ML problem, and important to society too.<br />
<br />
'''Oct. 25, 2016'''<br />
* Speaker: Douglas L. Jones<br />
* Time: 2:00<br />
* Affiliation: ECE Department, University of Illinois at Urbana-Champaign<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Optimal energy-efficient coding in sensory neurons<br />
* Abstract: Evolutionary pressure suggests that the spike-based code in the sensory nervous system should satisfy two opposing constraints: 1) minimize signal distortion in the encoding process (i.e., maintain fidelity) by keeping the average spike rate as high as possible, and 2) minimize the metabolic load on the neuron by keeping the average spike rate as low as possible. We hypothesize that selective pressure has shaped the biophysics of a neuron to satisfy these conflicting demands. An energy-fidelity trade-off can be obtained through a constrained optimization process that achieves the lowest signal distortion for a given constraint on the spike rate. We derive the asymptotically optimal average-energy-constrained neuronal source code and show that it leads to a dynamic threshold that functions as an internal decoder (reconstruction filter) and adapts a spike-firing threshold so that spikes are emitted only when the coding error reaches this threshold. A stochastic extension is obtained by adding internal noise (dithering, or stochastic resonance) to the spiking threshold. We show that the source-coding neuron model i) reproduces experimentally observed spike-times in response to a stimulus, and ii) reproduces the serial correlations in the observed sequence of inter-spike intervals, using data from a peripheral sensory neuron and a central (cortical) somatosensory neuron. Finally, we show that the spike-timing code, although a temporal code, is in the limit of high firing rates an instantaneous rate code and accurately predicts the peri-stimulus time histogram (PSTH). We conclude by suggesting possible biophysical (ionic) mechanisms for this coding scheme.<br />
<br />
'''October 26, 2016'''<br />
* Speaker: Eric Jonas<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Charles Frye<br />
* Status: confirmed<br />
* Title: Could a neuroscientist understand a microprocessor?<br />
* Abstract: There is a popular belief in neuroscience that we are primarily data limited, that producing large, multimodal, and complex datasets will, enabled by data analysis algorithms, lead to fundamental insights into the way the brain processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. Here we take a simulated classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the processor. This suggests that current computational approaches in neuroscience may fall short of producing meaningful models of the brain. We discuss several obvious shortcomings with this model, and ways that they might be addressed, both experimentally and computationally. <br />
* Bio: Eric Jonas is currently a postdoc in computer science at UC Berkeley working with Ben Recht on machine learning for scientific data acquisition. He earned his PhD in Computational Neuroscience, M. Eng in Electrical Engineering, BS in Electrical Engineering and Computer Science, and BS in Neurobiology, all from MIT. Prior to his return to academia, he was founder and CEO of Prior Knowledge, a predictive database company which was acquired in 2012 by Salesforce.com, where he was Chief Predictive Scientist until 2014. In 2015 he was named one of the top rising stars in bioengineering by the Defense Department’s Advanced Research Projects Agency (DARPA).<br />
<br />
'''Nov. 9, 2016'''<br />
* Speaker: Pulkit Agrawal<br />
* Time: 12:00<br />
* Affiliation: EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''Nov. 16, 2016'''<br />
* Speaker: Sebastian Musslick<br />
* Time: 12:00<br />
* Affiliation: Princeton Neuroscience Institute (Princeton University)<br />
* Host: Brian Cheung<br />
* Status: confirmed<br />
* Title: Parallel Processing Capability Versus Efficiency of Representation in Neural Network Architectures<br />
* Abstract: One of the most salient and well-recognized features of human goal-directed behavior is our limited ability to conduct multiple demanding tasks at once. Why is this? Some have suggested it reflects metabolic limitations, or structural ones. However, both explanations are unlikely. The brain routinely demonstrates the ability to carry out a multitude of processes in an enduring and parallel manner (walking, breathing, listening). Why, in contrast, is its capacity for allocating attention to control-demanding tasks - such a critical and powerful function - so limited? In the first part of my talk I will describe a computational framework that explains limitations of parallel processing in neural network architectures as the result of cross-talk between shared task representations. Using graph-theoretic analyses we show that the parallel processing (multitasking) capability of two-layer networks drops precipitously as a function of task pathway overlap, and scales highly sublinearly with network size. I will describe how this analysis can be applied to task representations encoded in neural networks or neuroimaging data, and show how it can be used to predict both concurrent and sequential multitasking performance in trained neural networks based on single task representations. Our results suggest that maximal parallel processing performance is achieved by segregating task pathways, by separating the representations on which they rely. However, there is a countervailing pressure for pathways to intersect: the re-use of representations to facilitate learning of new tasks. In the second part of my talk I will demonstrate a tradeoff between learning efficiency and parallel processing capability in neural networks. It can be shown that weight priors on learned task similarity improve learning speed and generalization but lead to strong constraints on parallel processing capability. These findings will be contrasted with an ongoing behavioral study by assessing learning and multitasking performance of human subjects across tasks with varying degrees of feature-overlap.<br />
<br />
'''Nov 30, 2016'''<br />
* Speaker: Marcus Rohrbach<br />
* Time: 12:00<br />
* Affiliation: EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title: Sparse Coding ASIC Chips for Feature Extraction and Classification<br />
* Abstract: Hardware-based computer vision accelerators will be an essential part of future mobile and autonomous devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to the underlying algorithms, which is an advantage over software-based solutions and general-purpose hardware. In this talk, I will present three application-specific integrated circuit (ASIC) chips that implement the sparse and independent local network (SAILnet) algorithm and the locally competitive algorithm (LCA) for feature extraction and classification. Two of the chips were designed using an array of leaky integrate-and-fire neurons. Sparse activations of the neurons make possible an efficient grid-ring architecture to deliver an image processing throughput of 1 G pixel/s using only 200 mW. The third chip was designed using a convolution approach. Sparsity is again an important factor that enabled the use of sparse convolvers to achieve an effective performance of 900 G operations/s using less than 150 mW.<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
'''June 8, 2016'''<br />
* Speaker: Kris Bouchard<br />
* Time: 12:00<br />
* Affiliation: LBNL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: The union of intersections method<br />
* Abstract:<br />
<br />
'''June 15, 2016'''<br />
* Speaker: James Blackmon<br />
* Time: 12:00<br />
* Affiliation: San Francisco State University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8723Seminars2017-03-06T23:53:52Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
'''March 1st, 2017'''<br />
* Speaker: Sahar Akram<br />
* Time: 12:00<br />
* Affiliation: Starkey Hearing Research Center <br />
* Host: Shariq<br />
* Status: Confirmed<br />
* Title: Real-Time & Adaptive Auditory Neural Processing<br />
* Abstract: Decoding the dynamics of brain activity underlying conscious behavior is one of the key questions in systems neuroscience. Sensory neurons, such as those in the auditory system, can undergo rapid and task-dependent changes in their response characteristics during attentive behavior, and thereby result in functional changes in the system over time. In order to quantify human’s conscious experience, neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) are widely used to record the neural activity from the brain with millisecond temporal resolution. Therefore, a dynamic decoding framework on par with the sampling resolution of EEG/MEG is crucial in order to better understand the neural correlates underlying sophisticated cognitive functions such as attention. I will talk about two recent attempts on real-time decoding of brain neural activity during a competing auditory attention task, using Bayesian hierarchical modeling and adaptive signal processing.<br />
<br />
'''Mar 2, 2017'''<br />
* Speaker: Joszef Fiser<br />
* Time: 12:00<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Mar 2, 2017'''<br />
* Speaker: Michael Frank<br />
* Time: 12:00<br />
* Affiliation: Magicore Systems<br />
* Host: Dylan<br />
* Status: Tentative date - unconfirmed<br />
* Title: The Future of the Multi-core Platform Task-Superscalar Extensions to Von-Neumann Architecture and Optimization for Neural Networks<br />
* Abstract: Technology scaling had been carrying computer science thru the second half of the 20th century until single CPU performance started leveling off, after which multi- and many-core processors, including GPUs, emerged as the substrate for high performance computing. Mobile market implementations followed this trend and today you might be carrying a phone with more than 16 different processors. For power efficiency reasons, many of the cores are specialized to perform limited functions (such as modem or connectivity control, graphics rendering, or future neural-network acceleration) with most mainstream phones containing four or more general purpose processors. As Steve Jobs insightfully commented almost a decade ago, “The way the processor industry is going is to add more and more cores, but nobody knows how to program those things.” Jobs was correct, programming these multiprocessor systems has become a challenge and several programming models have been proposed in academia to address this issue. Power and thermals are also an ever present thorn to mass market applications. Through the years, CPUs based on the von-Neumann architecture have fended off attacks from many directions; today complex super-scalar implementations execute multiple instructions each clock cycle, parallel and out-of-order, keeping up the illusion of sequential processing. Recent research demonstrates though that augmenting the paradigm of the Von-Neumann architecture with a few established concepts from data-flow and task-parallel programming, will create both a credible and intuitive parallel architecture enabling notable compute efficiency improvement while retaining compatibility with the current mainstream. This talk will thus review the current state of the processor industry and after highlighting why we are running out of steam in ILP; I will outline the task-superscalar programming model as the “ring to rule them all” and provide insights as to how this architecture can take advantage of special HW acceleration for data-flow management and provide support for efficient neuromorphic computing.<br />
<br />
'''April 12, 2017'''<br />
* Speaker: Aapo Hyvarinen<br />
* Time: 12:00<br />
* Affiliation: Gatsby/UCL<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''TBD, 2016'''<br />
* Speaker: Alexander Stubbs<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Michael Levy<br />
* Status: tentative<br />
* Title: Could chromatic aberration allow for an alternative evolutionary pathway towards color vision?<br />
* Abstract: We present a mechanism by which organisms with only a single photoreceptor, which have a monochromatic view of the world, can achieve color discrimination. An off-axis pupil and the principle of chromatic aberration (where different wavelengths come to focus at different distances behind a lens) can combine to provide “color-blind” animals with a way to distinguish colors. As a specific example, we constructed a computer model of the visual system of cephalopods (octopus, squid, and cuttlefish) that have a single unfiltered photoreceptor type. We compute a quantitative image quality budget for this visual system and show how chromatic blurring dominates the visual acuity in these animals in shallow water. This proposed mechanism is consistent with the extensive suite of visual/behavioral and physiological data that has been obtained from cephalopod studies and offers a possible solution to the apparent paradox of vivid chromatic behaviors in color blind animals. Moreover, this proposed mechanism has potential applicability in organisms with limited photoreceptor complements, such as spiders and dolphins.<br />
<br />
'''Sept. 4 or 11, 2017'''<br />
* Speaker: Carl Pabo<br />
* Time: 12:00<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2016/17 academic year ===<br />
<br />
'''Sept. 7, 2016'''<br />
* Speaker: Dan Stowell<br />
* Time: 12:00<br />
* Affiliation: Queen Mary, University of London<br />
* Host: Frederic Theunissen<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 8, 2016'''<br />
* Speaker: Barb Finlay<br />
* Time: 12:00<br />
* Affiliation: Cornell Univ<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 27, 2016'''<br />
* Speaker: Yoshua Bengio<br />
* Time: 11:00<br />
* Affiliation: Univ Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Oct. 12, 2016'''<br />
* Speaker: Paul Rhodes<br />
* Time: 4:00<br />
* Affiliation: Specific Technologies<br />
* Host: Dylan/Bruno<br />
* Status: confirmed<br />
* Title: A novel and important problem in spatiotemporal pattern classification<br />
* Abstract: Specific Technologies uses a sensor response that consists of a vector time series, a spatiotemporal fingerprint, to classify bacteria at the strain level during their growth. The identification of resistant strains of bacteria has become one of the world's great problems (here is a link to a $20M prize that the US govt has issued: https://www.nih.gov/news-events/news-releases/federal-prize-competition-seeks-innovative-ideas-combat-antimicrobial-resistance). We are using deep convolutional nets to do this classification, but they are instantaneous, and so do not capture the temporal patterns that are often at the core of what differentiates strains. So using the full temporal character of the sensor response time series is a cutting edge neural ML problem, and important to society too.<br />
<br />
'''Oct. 25, 2016'''<br />
* Speaker: Douglas L. Jones<br />
* Time: 2:00<br />
* Affiliation: ECE Department, University of Illinois at Urbana-Champaign<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Optimal energy-efficient coding in sensory neurons<br />
* Abstract: Evolutionary pressure suggests that the spike-based code in the sensory nervous system should satisfy two opposing constraints: 1) minimize signal distortion in the encoding process (i.e., maintain fidelity) by keeping the average spike rate as high as possible, and 2) minimize the metabolic load on the neuron by keeping the average spike rate as low as possible. We hypothesize that selective pressure has shaped the biophysics of a neuron to satisfy these conflicting demands. An energy-fidelity trade-off can be obtained through a constrained optimization process that achieves the lowest signal distortion for a given constraint on the spike rate. We derive the asymptotically optimal average-energy-constrained neuronal source code and show that it leads to a dynamic threshold that functions as an internal decoder (reconstruction filter) and adapts a spike-firing threshold so that spikes are emitted only when the coding error reaches this threshold. A stochastic extension is obtained by adding internal noise (dithering, or stochastic resonance) to the spiking threshold. We show that the source-coding neuron model i) reproduces experimentally observed spike-times in response to a stimulus, and ii) reproduces the serial correlations in the observed sequence of inter-spike intervals, using data from a peripheral sensory neuron and a central (cortical) somatosensory neuron. Finally, we show that the spike-timing code, although a temporal code, is in the limit of high firing rates an instantaneous rate code and accurately predicts the peri-stimulus time histogram (PSTH). We conclude by suggesting possible biophysical (ionic) mechanisms for this coding scheme.<br />
<br />
'''October 26, 2016'''<br />
* Speaker: Eric Jonas<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Charles Frye<br />
* Status: confirmed<br />
* Title: Could a neuroscientist understand a microprocessor?<br />
* Abstract: There is a popular belief in neuroscience that we are primarily data limited, that producing large, multimodal, and complex datasets will, enabled by data analysis algorithms, lead to fundamental insights into the way the brain processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. Here we take a simulated classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the processor. This suggests that current computational approaches in neuroscience may fall short of producing meaningful models of the brain. We discuss several obvious shortcomings with this model, and ways that they might be addressed, both experimentally and computationally. <br />
* Bio: Eric Jonas is currently a postdoc in computer science at UC Berkeley working with Ben Recht on machine learning for scientific data acquisition. He earned his PhD in Computational Neuroscience, M. Eng in Electrical Engineering, BS in Electrical Engineering and Computer Science, and BS in Neurobiology, all from MIT. Prior to his return to academia, he was founder and CEO of Prior Knowledge, a predictive database company which was acquired in 2012 by Salesforce.com, where he was Chief Predictive Scientist until 2014. In 2015 he was named one of the top rising stars in bioengineering by the Defense Department’s Advanced Research Projects Agency (DARPA).<br />
<br />
'''Nov. 9, 2016'''<br />
* Speaker: Pulkit Agrawal<br />
* Time: 12:00<br />
* Affiliation: EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''Nov. 16, 2016'''<br />
* Speaker: Sebastian Musslick<br />
* Time: 12:00<br />
* Affiliation: Princeton Neuroscience Institute (Princeton University)<br />
* Host: Brian Cheung<br />
* Status: confirmed<br />
* Title: Parallel Processing Capability Versus Efficiency of Representation in Neural Network Architectures<br />
* Abstract: One of the most salient and well-recognized features of human goal-directed behavior is our limited ability to conduct multiple demanding tasks at once. Why is this? Some have suggested it reflects metabolic limitations, or structural ones. However, both explanations are unlikely. The brain routinely demonstrates the ability to carry out a multitude of processes in an enduring and parallel manner (walking, breathing, listening). Why, in contrast, is its capacity for allocating attention to control-demanding tasks - such a critical and powerful function - so limited? In the first part of my talk I will describe a computational framework that explains limitations of parallel processing in neural network architectures as the result of cross-talk between shared task representations. Using graph-theoretic analyses we show that the parallel processing (multitasking) capability of two-layer networks drops precipitously as a function of task pathway overlap, and scales highly sublinearly with network size. I will describe how this analysis can be applied to task representations encoded in neural networks or neuroimaging data, and show how it can be used to predict both concurrent and sequential multitasking performance in trained neural networks based on single task representations. Our results suggest that maximal parallel processing performance is achieved by segregating task pathways, by separating the representations on which they rely. However, there is a countervailing pressure for pathways to intersect: the re-use of representations to facilitate learning of new tasks. In the second part of my talk I will demonstrate a tradeoff between learning efficiency and parallel processing capability in neural networks. It can be shown that weight priors on learned task similarity improve learning speed and generalization but lead to strong constraints on parallel processing capability. These findings will be contrasted with an ongoing behavioral study by assessing learning and multitasking performance of human subjects across tasks with varying degrees of feature-overlap.<br />
<br />
'''Nov 30, 2016'''<br />
* Speaker: Marcus Rohrbach<br />
* Time: 12:00<br />
* Affiliation: EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title: Sparse Coding ASIC Chips for Feature Extraction and Classification<br />
* Abstract: Hardware-based computer vision accelerators will be an essential part of future mobile and autonomous devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to the underlying algorithms, which is an advantage over software-based solutions and general-purpose hardware. In this talk, I will present three application-specific integrated circuit (ASIC) chips that implement the sparse and independent local network (SAILnet) algorithm and the locally competitive algorithm (LCA) for feature extraction and classification. Two of the chips were designed using an array of leaky integrate-and-fire neurons. Sparse activations of the neurons make possible an efficient grid-ring architecture to deliver an image processing throughput of 1 G pixel/s using only 200 mW. The third chip was designed using a convolution approach. Sparsity is again an important factor that enabled the use of sparse convolvers to achieve an effective performance of 900 G operations/s using less than 150 mW.<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
'''June 8, 2016'''<br />
* Speaker: Kris Bouchard<br />
* Time: 12:00<br />
* Affiliation: LBNL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: The union of intersections method<br />
* Abstract:<br />
<br />
'''June 15, 2016'''<br />
* Speaker: James Blackmon<br />
* Time: 12:00<br />
* Affiliation: San Francisco State University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8722Seminars2017-03-06T23:50:20Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
'''March 1st, 2017'''<br />
* Speaker: Sahar Akram<br />
* Time: 12:00<br />
* Affiliation: Starkey Hearing Research Center <br />
* Host: Shariq<br />
* Status: Confirmed<br />
* Title: Real-Time & Adaptive Auditory Neural Processing<br />
* Abstract: Decoding the dynamics of brain activity underlying conscious behavior is one of the key questions in systems neuroscience. Sensory neurons, such as those in the auditory system, can undergo rapid and task-dependent changes in their response characteristics during attentive behavior, and thereby result in functional changes in the system over time. In order to quantify human’s conscious experience, neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) are widely used to record the neural activity from the brain with millisecond temporal resolution. Therefore, a dynamic decoding framework on par with the sampling resolution of EEG/MEG is crucial in order to better understand the neural correlates underlying sophisticated cognitive functions such as attention. I will talk about two recent attempts on real-time decoding of brain neural activity during a competing auditory attention task, using Bayesian hierarchical modeling and adaptive signal processing.<br />
<br />
'''Mar 2, 2017'''<br />
* Speaker: Joszef Fiser<br />
* Time: 12:00<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Mar 2, 2017'''<br />
* Speaker: Michael Frank<br />
* Time: 12:00<br />
* Affiliation: Magicore Systems<br />
* Host: Dylan<br />
* Status: Tentative date - unconfirmed<br />
* Title: The Future of the Multi-core Platform Task-Superscalar Extensions to Von-Neumann Architecture and Optimization for Neural Networks<br />
* Abstract:<br />
<br />
'''April 12, 2017'''<br />
* Speaker: Aapo Hyvarinen<br />
* Time: 12:00<br />
* Affiliation: Gatsby/UCL<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''TBD, 2016'''<br />
* Speaker: Alexander Stubbs<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Michael Levy<br />
* Status: tentative<br />
* Title: Could chromatic aberration allow for an alternative evolutionary pathway towards color vision?<br />
* Abstract: We present a mechanism by which organisms with only a single photoreceptor, which have a monochromatic view of the world, can achieve color discrimination. An off-axis pupil and the principle of chromatic aberration (where different wavelengths come to focus at different distances behind a lens) can combine to provide “color-blind” animals with a way to distinguish colors. As a specific example, we constructed a computer model of the visual system of cephalopods (octopus, squid, and cuttlefish) that have a single unfiltered photoreceptor type. We compute a quantitative image quality budget for this visual system and show how chromatic blurring dominates the visual acuity in these animals in shallow water. This proposed mechanism is consistent with the extensive suite of visual/behavioral and physiological data that has been obtained from cephalopod studies and offers a possible solution to the apparent paradox of vivid chromatic behaviors in color blind animals. Moreover, this proposed mechanism has potential applicability in organisms with limited photoreceptor complements, such as spiders and dolphins.<br />
<br />
'''Sept. 4 or 11, 2017'''<br />
* Speaker: Carl Pabo<br />
* Time: 12:00<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2016/17 academic year ===<br />
<br />
'''Sept. 7, 2016'''<br />
* Speaker: Dan Stowell<br />
* Time: 12:00<br />
* Affiliation: Queen Mary, University of London<br />
* Host: Frederic Theunissen<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 8, 2016'''<br />
* Speaker: Barb Finlay<br />
* Time: 12:00<br />
* Affiliation: Cornell Univ<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 27, 2016'''<br />
* Speaker: Yoshua Bengio<br />
* Time: 11:00<br />
* Affiliation: Univ Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Oct. 12, 2016'''<br />
* Speaker: Paul Rhodes<br />
* Time: 4:00<br />
* Affiliation: Specific Technologies<br />
* Host: Dylan/Bruno<br />
* Status: confirmed<br />
* Title: A novel and important problem in spatiotemporal pattern classification<br />
* Abstract: Specific Technologies uses a sensor response that consists of a vector time series, a spatiotemporal fingerprint, to classify bacteria at the strain level during their growth. The identification of resistant strains of bacteria has become one of the world's great problems (here is a link to a $20M prize that the US govt has issued: https://www.nih.gov/news-events/news-releases/federal-prize-competition-seeks-innovative-ideas-combat-antimicrobial-resistance). We are using deep convolutional nets to do this classification, but they are instantaneous, and so do not capture the temporal patterns that are often at the core of what differentiates strains. So using the full temporal character of the sensor response time series is a cutting edge neural ML problem, and important to society too.<br />
<br />
'''Oct. 25, 2016'''<br />
* Speaker: Douglas L. Jones<br />
* Time: 2:00<br />
* Affiliation: ECE Department, University of Illinois at Urbana-Champaign<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Optimal energy-efficient coding in sensory neurons<br />
* Abstract: Evolutionary pressure suggests that the spike-based code in the sensory nervous system should satisfy two opposing constraints: 1) minimize signal distortion in the encoding process (i.e., maintain fidelity) by keeping the average spike rate as high as possible, and 2) minimize the metabolic load on the neuron by keeping the average spike rate as low as possible. We hypothesize that selective pressure has shaped the biophysics of a neuron to satisfy these conflicting demands. An energy-fidelity trade-off can be obtained through a constrained optimization process that achieves the lowest signal distortion for a given constraint on the spike rate. We derive the asymptotically optimal average-energy-constrained neuronal source code and show that it leads to a dynamic threshold that functions as an internal decoder (reconstruction filter) and adapts a spike-firing threshold so that spikes are emitted only when the coding error reaches this threshold. A stochastic extension is obtained by adding internal noise (dithering, or stochastic resonance) to the spiking threshold. We show that the source-coding neuron model i) reproduces experimentally observed spike-times in response to a stimulus, and ii) reproduces the serial correlations in the observed sequence of inter-spike intervals, using data from a peripheral sensory neuron and a central (cortical) somatosensory neuron. Finally, we show that the spike-timing code, although a temporal code, is in the limit of high firing rates an instantaneous rate code and accurately predicts the peri-stimulus time histogram (PSTH). We conclude by suggesting possible biophysical (ionic) mechanisms for this coding scheme.<br />
<br />
'''October 26, 2016'''<br />
* Speaker: Eric Jonas<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Charles Frye<br />
* Status: confirmed<br />
* Title: Could a neuroscientist understand a microprocessor?<br />
* Abstract: There is a popular belief in neuroscience that we are primarily data limited, that producing large, multimodal, and complex datasets will, enabled by data analysis algorithms, lead to fundamental insights into the way the brain processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. Here we take a simulated classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the processor. This suggests that current computational approaches in neuroscience may fall short of producing meaningful models of the brain. We discuss several obvious shortcomings with this model, and ways that they might be addressed, both experimentally and computationally. <br />
* Bio: Eric Jonas is currently a postdoc in computer science at UC Berkeley working with Ben Recht on machine learning for scientific data acquisition. He earned his PhD in Computational Neuroscience, M. Eng in Electrical Engineering, BS in Electrical Engineering and Computer Science, and BS in Neurobiology, all from MIT. Prior to his return to academia, he was founder and CEO of Prior Knowledge, a predictive database company which was acquired in 2012 by Salesforce.com, where he was Chief Predictive Scientist until 2014. In 2015 he was named one of the top rising stars in bioengineering by the Defense Department’s Advanced Research Projects Agency (DARPA).<br />
<br />
'''Nov. 9, 2016'''<br />
* Speaker: Pulkit Agrawal<br />
* Time: 12:00<br />
* Affiliation: EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''Nov. 16, 2016'''<br />
* Speaker: Sebastian Musslick<br />
* Time: 12:00<br />
* Affiliation: Princeton Neuroscience Institute (Princeton University)<br />
* Host: Brian Cheung<br />
* Status: confirmed<br />
* Title: Parallel Processing Capability Versus Efficiency of Representation in Neural Network Architectures<br />
* Abstract: One of the most salient and well-recognized features of human goal-directed behavior is our limited ability to conduct multiple demanding tasks at once. Why is this? Some have suggested it reflects metabolic limitations, or structural ones. However, both explanations are unlikely. The brain routinely demonstrates the ability to carry out a multitude of processes in an enduring and parallel manner (walking, breathing, listening). Why, in contrast, is its capacity for allocating attention to control-demanding tasks - such a critical and powerful function - so limited? In the first part of my talk I will describe a computational framework that explains limitations of parallel processing in neural network architectures as the result of cross-talk between shared task representations. Using graph-theoretic analyses we show that the parallel processing (multitasking) capability of two-layer networks drops precipitously as a function of task pathway overlap, and scales highly sublinearly with network size. I will describe how this analysis can be applied to task representations encoded in neural networks or neuroimaging data, and show how it can be used to predict both concurrent and sequential multitasking performance in trained neural networks based on single task representations. Our results suggest that maximal parallel processing performance is achieved by segregating task pathways, by separating the representations on which they rely. However, there is a countervailing pressure for pathways to intersect: the re-use of representations to facilitate learning of new tasks. In the second part of my talk I will demonstrate a tradeoff between learning efficiency and parallel processing capability in neural networks. It can be shown that weight priors on learned task similarity improve learning speed and generalization but lead to strong constraints on parallel processing capability. These findings will be contrasted with an ongoing behavioral study by assessing learning and multitasking performance of human subjects across tasks with varying degrees of feature-overlap.<br />
<br />
'''Nov 30, 2016'''<br />
* Speaker: Marcus Rohrbach<br />
* Time: 12:00<br />
* Affiliation: EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title: Sparse Coding ASIC Chips for Feature Extraction and Classification<br />
* Abstract: Hardware-based computer vision accelerators will be an essential part of future mobile and autonomous devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to the underlying algorithms, which is an advantage over software-based solutions and general-purpose hardware. In this talk, I will present three application-specific integrated circuit (ASIC) chips that implement the sparse and independent local network (SAILnet) algorithm and the locally competitive algorithm (LCA) for feature extraction and classification. Two of the chips were designed using an array of leaky integrate-and-fire neurons. Sparse activations of the neurons make possible an efficient grid-ring architecture to deliver an image processing throughput of 1 G pixel/s using only 200 mW. The third chip was designed using a convolution approach. Sparsity is again an important factor that enabled the use of sparse convolvers to achieve an effective performance of 900 G operations/s using less than 150 mW.<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
'''June 8, 2016'''<br />
* Speaker: Kris Bouchard<br />
* Time: 12:00<br />
* Affiliation: LBNL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: The union of intersections method<br />
* Abstract:<br />
<br />
'''June 15, 2016'''<br />
* Speaker: James Blackmon<br />
* Time: 12:00<br />
* Affiliation: San Francisco State University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8649TCN2016-10-19T21:34:55Z<p>Dpaiton: /* Fall 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer (Fall 2016) [mailto:s.mackesey@gmail.com?Subject=Redwood%TCN%Journal%20Club Sean Mackesey]<br />
<br />
=== Time and Location ===<br />
(Fall 2016)<br />
'''4pm-5pm''' every Monday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on changes to meeting dates.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, send an email to redwood_tcn+subscribe@lists.berkeley.edu. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
===Fall 2016===<br />
<br />
* [Dec 12] <font color="##ff0000">Charles Frye</font> - <br />
* [Dec 05] <font color="##ff0000">Dylan Paiton</font> - JR Golden et al (2016). Conjectures Regarding the Nonlinear Geometry of Visual Neurons. [http://www.sciencedirect.com/science/article/pii/S0042698915003600]<br />
* [Nov 28] <font color="##ff0000">Chris Warner</font> - <br />
* [Nov 21] <font color="##ff0000">Kohta Ishikawa</font> - <br />
* [Nov 14] <font color="##ff0000">Eric Dodds</font> - <br />
* [Nov 07] <font color="##ff0000">Petr Jezek</font> - <br />
* [Oct 31] <font color="##ff0000">Yubei Chen</font> - <br />
* [Oct 24] <font color="##ff0000">Ryan Zarcone</font> - <br />
* [Oct 17] <font color="##ff0000">Neha Wadia</font> - Rajan et al (2016). Recurrent network models of sequence generation and memory. [http://www.sciencedirect.com/science/article/pii/S0896627316001021]<br />
* [Oct 10] <font color="##ff0000">Eric Weiss</font> - Tutorial: Hyperdimensional Representations for Scene Analysis.<br />
* [Oct 03] <font color="##ff0000">Vasha Dutell</font> - Dong & Atick (1995). Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus. [http://www.tandfonline.com/doi/abs/10.1088/0954-898X_6_2_003]<br />
* [Sep 26] <font color="##ff0000">Jesse Livezey</font> - Cheung et al (2016). The auditory representation of speech sounds in human motor cortex. [https://elifesciences.org/content/5/e12577]<br />
* [Sep 19] <font color="##ff0000">Brian Cheung</font> - Discussion: The Biological Plausibility of Backpropagation.<br />
* [Sep 12] <font color="##ff0000">Alex Anderson</font> - Jaderberg et al (2016). Decoupled neural interfaces using synthetic gradients. [https://arxiv.org/abs/1608.05343]<br />
* [Sep 05] <font color="##ff0000">Charles Garfinkle</font> - Palmer et al (2015). Predictive information in a sensory population. [http://www.pnas.org/content/112/22/6908.full]<br />
<br />
===Summer 2016===<br />
* [August 31] <font color="##ff0000">Spencer Kent</font> - Fu et al (2016). Occlusion boundary detection via deep exploration of context. [https://ps.is.tuebingen.mpg.de/publications/fu-cvpr-2016]<br />
* [August 17] <font color="#ff0000">Paxon Frady</font> - Glasser, M. F., et al. (2016) A Multi-modal parcellation of human cerebral cortex. [http://www.nature.com/nature/journal/v536/n7615/pdf/nature18933.pdf] and Huth, Alexander G., et al. (2016) Natural speech reveals the semantic maps that tile human cerebral cortex. [http://www.nature.com/nature/journal/v532/n7600/pdf/nature17637.pdf]<br />
* [August 10] <font color="#ff0000">Mayur Mudigonda</font> - Salimans, Tim, Diederik P. Kingma, and Max Welling (2015). Markov chain Monte Carlo and variational inference: Bridging the gap. [http://www.jmlr.org/proceedings/papers/v37/salimans15.pdf]<br />
* [August 3] <font color="#ff0000">Pratik Sachdeva</font> - Moreno-Bote, Rubén, et al. (2014). Information-limiting correlations. [http://www.nature.com/neuro/journal/v17/n10/pdf/nn.3807.pdf]<br />
* [July 26] <font color="#ff0000">Shariq Mobin</font> - Karklin, Yan, and Eero P. Simoncelli (2011). Efficient coding of natural images with a population of noisy linear-nonlinear neurons.[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4532291/pdf/nihms711099.pdf]<br />
* [July 20] <font color="#ff0000">Sean Mackesey</font> - Buzsáki, György (2010). Neural syntax: cell assemblies, synapsembles, and readers. [http://www.cell.com/neuron/pdf/S0896-6273(10)00765-8.pdf]<br />
* [July 13] <font color="#ff0000">Katarina Slama and Vasha DuTell</font> - Akam, Thomas, and Dimitri M. Kullmann (2010). Oscillations and filtering networks support flexible routing of information. [http://ac.els-cdn.com/S0896627310004770/1-s2.0-S0896627310004770-main.pdf?_tid=67abdb2c-448b-11e6-8094-00000aacb35d&acdnat=1467927801_0eb47d2d6165fef5d1a1ae39b1b927b2]<br />
* [July 6] <font color="#ff0000">Karl Zipser</font> - Bojarski, Mariusz, et al (2016). End to End Learning for Self-Driving Cars. [https://arxiv.org/pdf/1604.07316v1.pdf]<br />
* [June 29] <font color="#ff0000">Ian Robertson</font> - Hermans, M., & Van Vaerenbergh, T. (2015). Towards Trainable Media: Using Waves for Neural Network-Style Training. [http://arxiv.org/pdf/1510.03776v1.pdf]<br />
* [June 22] <font color="#ff0000">Group Discussion</font> - Jonas, Eric, and Konrad Kording. (2016) Could a neuroscientist understand a microprocessor? [http://biorxiv.org/content/biorxiv/early/2016/05/26/055624.full.pdf]<br />
* [June 15] <font color="#ff0000">Spencer Kent, Mayur Mudigonda, and Eric Weiss</font> - Kulkarni, Tejas D., et al (2015). Picture: A probabilistic programming language for scene perception. [https://mrkulk.github.io/www_cvpr15/1999.pdf]<br />
* [June 8] <font color="#ff0000">Jesse Livezey</font> - Bornschein, Jörg, Marc Henniges, and Jörg Lücke (2013). Are V1 simple cells optimized for visual occlusions? A comparative study. [http://journals.plos.org/ploscompbiol/article/asset?id=10.1371%2Fjournal.pcbi.1003062.PDF]<br />
* [June 1] <font color="#ff0000">Yubei Chen</font> - Y. Karklin & M. S. Lewicki (2003). Learning higher-order structures in natural images. [http://yan.karklin.com/pubs/Karklin-Lewicki-NCNS03.pdf]<br />
* [May 25] <font color="#ff0000">Eric Dodds</font> - Y. Karklin, C. Ekanadham, & E.P. Simoncelli (2012). Hierarchical spike coding of sound. [http://papers.nips.cc/paper/4780-hierarchical-spike-coding-of-sound.pdf]<br />
<br />
===Spring 2016===<br />
<br />
* [May 11] <font color="#ff0000">NIPS exchange</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font> - MEJ Newman (2006). Finding Community Structure in Networks using the Eigenvectors of Matrices [http://arxiv.org/pdf/physics/0605087.pdf]<br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font> - E Ahissar, A Arieli (2012). Seeing Via Miniature Eye Movements- A Dynamic Hypothesis for Vision [http://www.frontiersin.org/Journal/DownloadFile/1/91957/32594/1/21/fncom-06-00089_pdf]<br />
<br />
* [Apr 20] <font color="#ff0000">Charles Frye, Ryan Zarcone, Brian Cheung</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]<br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8620Seminars2016-09-28T20:41:08Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
'''Oct/Nov. TBD, 2016'''<br />
* Speaker: Alexander Stubbs<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Michael Levy<br />
* Status: tentative<br />
* Title: Could chromatic aberration allow for an alternative evolutionary pathway towards color vision?<br />
* Abstract: We present a mechanism by which organisms with only a single photoreceptor, which have a monochromatic view of the world, can achieve color discrimination. An off-axis pupil and the principle of chromatic aberration (where different wavelengths come to focus at different distances behind a lens) can combine to provide “color-blind” animals with a way to distinguish colors. As a specific example, we constructed a computer model of the visual system of cephalopods (octopus, squid, and cuttlefish) that have a single unfiltered photoreceptor type. We compute a quantitative image quality budget for this visual system and show how chromatic blurring dominates the visual acuity in these animals in shallow water. This proposed mechanism is consistent with the extensive suite of visual/behavioral and physiological data that has been obtained from cephalopod studies and offers a possible solution to the apparent paradox of vivid chromatic behaviors in color blind animals. Moreover, this proposed mechanism has potential applicability in organisms with limited photoreceptor complements, such as spiders and dolphins.<br />
<br />
'''Oct. 12, 2016'''<br />
* Speaker: Paul Rhodes<br />
* Time: 4:00<br />
* Affiliation: Specific Technologies<br />
* Host: Dylan/Bruno<br />
* Status: confirmed<br />
* Title: A novel and important problem in spatiotemporal pattern classification<br />
* Abstract: Specific Technologies uses a sensor response that consists of a vector time series, a spatiotemporal fingerprint, to classify bacteria at the strain level during their growth. The identification of resistant strains of bacteria has become one of the world's great problems (here is a link to a $20M prize that the US govt has issued: https://www.nih.gov/news-events/news-releases/federal-prize-competition-seeks-innovative-ideas-combat-antimicrobial-resistance). We are using deep convolutional nets to do this classification, but they are instantaneous, and so do not capture the temporal patterns that are often at the core of what differentiates strains. So using the full temporal character of the sensor response time series is a cutting edge neural ML problem, and important to society too.<br />
<br />
'''October 19, 2016'''<br />
* Speaker: Nihat Ay<br />
* Time: 12:00<br />
* Affiliation: Max Planck Leipzig<br />
* Host: Bruno/Max<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 26, 2016'''<br />
* Speaker: Eric Jonas<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Charles Frye<br />
* Status: confirmed<br />
* Title: Could a neuroscientist understand a microprocessor?<br />
* Abstract:<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2016/17 academic year ===<br />
<br />
'''Sept. 7, 2016'''<br />
* Speaker: Dan Stowell<br />
* Time: 12:00<br />
* Affiliation: Queen Mary, University of London<br />
* Host: Frederic Theunissen<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 8, 2016'''<br />
* Speaker: Barb Finlay<br />
* Time: 12:00<br />
* Affiliation: Cornell Univ<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 27, 2016'''<br />
* Speaker: Yoshua Bengio<br />
* Time: 11:00<br />
* Affiliation: Univ Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title: Sparse Coding ASIC Chips for Feature Extraction and Classification<br />
* Abstract: Hardware-based computer vision accelerators will be an essential part of future mobile and autonomous devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to the underlying algorithms, which is an advantage over software-based solutions and general-purpose hardware. In this talk, I will present three application-specific integrated circuit (ASIC) chips that implement the sparse and independent local network (SAILnet) algorithm and the locally competitive algorithm (LCA) for feature extraction and classification. Two of the chips were designed using an array of leaky integrate-and-fire neurons. Sparse activations of the neurons make possible an efficient grid-ring architecture to deliver an image processing throughput of 1 G pixel/s using only 200 mW. The third chip was designed using a convolution approach. Sparsity is again an important factor that enabled the use of sparse convolvers to achieve an effective performance of 900 G operations/s using less than 150 mW.<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
'''June 8, 2016'''<br />
* Speaker: Kris Bouchard<br />
* Time: 12:00<br />
* Affiliation: LBNL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: The union of intersections method<br />
* Abstract:<br />
<br />
'''June 15, 2016'''<br />
* Speaker: James Blackmon<br />
* Time: 12:00<br />
* Affiliation: San Francisco State University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8612Seminars2016-09-27T16:50:30Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
'''Sept. 27, 2016'''<br />
* Speaker: Yoshua Bengio<br />
* Time: 11:00<br />
* Affiliation: Univ Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Oct/Nov. TBD, 2016'''<br />
* Speaker: Alexander Stubbs<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Michael Levy<br />
* Status: tentative<br />
* Title: Could chromatic aberration allow for an alternative evolutionary pathway towards color vision?<br />
* Abstract: We present a mechanism by which organisms with only a single photoreceptor, which have a monochromatic view of the world, can achieve color discrimination. An off-axis pupil and the principle of chromatic aberration (where different wavelengths come to focus at different distances behind a lens) can combine to provide “color-blind” animals with a way to distinguish colors. As a specific example, we constructed a computer model of the visual system of cephalopods (octopus, squid, and cuttlefish) that have a single unfiltered photoreceptor type. We compute a quantitative image quality budget for this visual system and show how chromatic blurring dominates the visual acuity in these animals in shallow water. This proposed mechanism is consistent with the extensive suite of visual/behavioral and physiological data that has been obtained from cephalopod studies and offers a possible solution to the apparent paradox of vivid chromatic behaviors in color blind animals. Moreover, this proposed mechanism has potential applicability in organisms with limited photoreceptor complements, such as spiders and dolphins.<br />
<br />
'''Oct. 05, 2016'''<br />
* Speaker: Paul Rhodes<br />
* Time: 12:00<br />
* Affiliation: Specific Technologies<br />
* Host: Dylan/Bruno<br />
* Status: unconfirmed<br />
* Title: A novel and important problem in spatiotemporal pattern classification<br />
* Abstract: Specific Technologies uses a sensor response that consists of a vector time series, a spatiotemporal fingerprint, to classify bacteria at the strain level during their growth. The identification of resistant strains of bacteria has become one of the world's great problems (here is a link to a $20M prize that the US govt has issued: https://www.nih.gov/news-events/news-releases/federal-prize-competition-seeks-innovative-ideas-combat-antimicrobial-resistance). We are using deep convolutional nets to do this classification, but they are instantaneous, and so do not capture the temporal patterns that are often at the core of what differentiates strains. So using the full temporal character of the sensor response time series is a cutting edge neural ML problem, and important to society too.<br />
<br />
'''October 19, 2016'''<br />
* Speaker: Nihat Ay<br />
* Time: 12:00<br />
* Affiliation: Max Planck Leipzig<br />
* Host: Bruno/Max<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2016/17 academic year ===<br />
<br />
'''Sept. 7, 2016'''<br />
* Speaker: Dan Stowell<br />
* Time: 12:00<br />
* Affiliation: Queen Mary, University of London<br />
* Host: Frederic Theunissen<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 8, 2016'''<br />
* Speaker: Barb Finlay<br />
* Time: 12:00<br />
* Affiliation: Cornell Univ<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title: Sparse Coding ASIC Chips for Feature Extraction and Classification<br />
* Abstract: Hardware-based computer vision accelerators will be an essential part of future mobile and autonomous devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to the underlying algorithms, which is an advantage over software-based solutions and general-purpose hardware. In this talk, I will present three application-specific integrated circuit (ASIC) chips that implement the sparse and independent local network (SAILnet) algorithm and the locally competitive algorithm (LCA) for feature extraction and classification. Two of the chips were designed using an array of leaky integrate-and-fire neurons. Sparse activations of the neurons make possible an efficient grid-ring architecture to deliver an image processing throughput of 1 G pixel/s using only 200 mW. The third chip was designed using a convolution approach. Sparsity is again an important factor that enabled the use of sparse convolvers to achieve an effective performance of 900 G operations/s using less than 150 mW.<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
'''June 8, 2016'''<br />
* Speaker: Kris Bouchard<br />
* Time: 12:00<br />
* Affiliation: LBNL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: The union of intersections method<br />
* Abstract:<br />
<br />
'''June 15, 2016'''<br />
* Speaker: James Blackmon<br />
* Time: 12:00<br />
* Affiliation: San Francisco State University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8610Seminars2016-09-26T21:30:55Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
'''Sept. 27, 2016'''<br />
* Speaker: Yoshua Bengio<br />
* Time: 11:00<br />
* Affiliation: Univ Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 28, 2016'''<br />
* Speaker: Alexander Stubbs<br />
* Time: 12:00<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Michael Levy<br />
* Status: tentative<br />
* Title: Could chromatic aberration allow for an alternative evolutionary pathway towards color vision?<br />
* Abstract: We present a mechanism by which organisms with only a single photoreceptor, which have a monochromatic view of the world, can achieve color discrimination. An off-axis pupil and the principle of chromatic aberration (where different wavelengths come to focus at different distances behind a lens) can combine to provide “color-blind” animals with a way to distinguish colors. As a specific example, we constructed a computer model of the visual system of cephalopods (octopus, squid, and cuttlefish) that have a single unfiltered photoreceptor type. We compute a quantitative image quality budget for this visual system and show how chromatic blurring dominates the visual acuity in these animals in shallow water. This proposed mechanism is consistent with the extensive suite of visual/behavioral and physiological data that has been obtained from cephalopod studies and offers a possible solution to the apparent paradox of vivid chromatic behaviors in color blind animals. Moreover, this proposed mechanism has potential applicability in organisms with limited photoreceptor complements, such as spiders and dolphins.<br />
<br />
'''Oct. 05, 2016'''<br />
* Speaker: Paul Rhodes<br />
* Time: 12:00<br />
* Affiliation: Specific Technologies<br />
* Host: Dylan/Bruno<br />
* Status: confirmed<br />
* Title: A novel and important problem in spatiotemporal pattern classification<br />
* Abstract: Specific Technologies uses a sensor response that consists of a vector time series, a spatiotemporal fingerprint, to classify bacteria at the strain level during their growth. The identification of resistant strains of bacteria has become one of the world's great problems (here is a link to a $20M prize that the US govt has issued: https://www.nih.gov/news-events/news-releases/federal-prize-competition-seeks-innovative-ideas-combat-antimicrobial-resistance). We are using deep convolutional nets to do this classification, but they are instantaneous, and so do not capture the temporal patterns that are often at the core of what differentiates strains. So using the full temporal character of the sensor response time series is a cutting edge neural ML problem, and important to society too.<br />
<br />
'''October 19, 2016'''<br />
* Speaker: Nihat Ay<br />
* Time: 12:00<br />
* Affiliation: Max Planck Leipzig<br />
* Host: Bruno/Max<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2016/17 academic year ===<br />
<br />
'''Sept. 7, 2016'''<br />
* Speaker: Dan Stowell<br />
* Time: 12:00<br />
* Affiliation: Queen Mary, University of London<br />
* Host: Frederic Theunissen<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept. 8, 2016'''<br />
* Speaker: Barb Finlay<br />
* Time: 12:00<br />
* Affiliation: Cornell Univ<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title: Sparse Coding ASIC Chips for Feature Extraction and Classification<br />
* Abstract: Hardware-based computer vision accelerators will be an essential part of future mobile and autonomous devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to the underlying algorithms, which is an advantage over software-based solutions and general-purpose hardware. In this talk, I will present three application-specific integrated circuit (ASIC) chips that implement the sparse and independent local network (SAILnet) algorithm and the locally competitive algorithm (LCA) for feature extraction and classification. Two of the chips were designed using an array of leaky integrate-and-fire neurons. Sparse activations of the neurons make possible an efficient grid-ring architecture to deliver an image processing throughput of 1 G pixel/s using only 200 mW. The third chip was designed using a convolution approach. Sparsity is again an important factor that enabled the use of sparse convolvers to achieve an effective performance of 900 G operations/s using less than 150 mW.<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
'''June 8, 2016'''<br />
* Speaker: Kris Bouchard<br />
* Time: 12:00<br />
* Affiliation: LBNL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: The union of intersections method<br />
* Abstract:<br />
<br />
'''June 15, 2016'''<br />
* Speaker: James Blackmon<br />
* Time: 12:00<br />
* Affiliation: San Francisco State University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8528TCN2016-05-13T01:00:02Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 18] <font color="#ff0000"></font><br />
<br />
* [May 11] <font color="#ff0000">NIPS exchange</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font> - MEJ Newman (2006). Finding Community Structure in Networks using the Eigenvectors of Matrices [http://arxiv.org/pdf/physics/0605087.pdf]<br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font> - E Ahissar, A Arieli (2012). Seeing Via Miniature Eye Movements- A Dynamic Hypothesis for Vision [http://www.frontiersin.org/Journal/DownloadFile/1/91957/32594/1/21/fncom-06-00089_pdf]<br />
<br />
* [Apr 20] <font color="#ff0000">Charles Frye, Ryan Zarcone, Brian Cheung</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]<br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8527TCN2016-05-09T16:12:13Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 18] <font color="#ff0000">Karl Zipser</font><br />
<br />
* [May 11] <font color="#ff0000">NIPS exchange</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font> - MEJ Newman (2006). Finding Community Structure in Networks using the Eigenvectors of Matrices [http://arxiv.org/pdf/physics/0605087.pdf]<br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font> - E Ahissar, A Arieli (2012). Seeing Via Miniature Eye Movements- A Dynamic Hypothesis for Vision [http://www.frontiersin.org/Journal/DownloadFile/1/91957/32594/1/21/fncom-06-00089_pdf]<br />
<br />
* [Apr 20] <font color="#ff0000">Charles Frye, Ryan Zarcone, Brian Cheung</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]<br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8522TCN2016-04-27T23:53:06Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 18] <font color="#ff0000">Dylan Paiton</font><br />
<br />
* [May 11] <font color="#ff0000">Karl Zipser</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font> - E Ahissar, A Arieli (2012). Seeing Via Miniature Eye Movements- A Dynamic Hypothesis for Vision [http://www.frontiersin.org/Journal/DownloadFile/1/91957/32594/1/21/fncom-06-00089_pdf]<br />
<br />
* [Apr 20] <font color="#ff0000">Charles Frye, Ryan Zarcone, Brian Cheung</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]<br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8520TCN2016-04-21T23:45:07Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 18] <font color="#ff0000">Dylan Paiton</font><br />
<br />
* [May 11] <font color="#ff0000">Karl Zipser</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font> - E Ahissar, A Arieli (2012). Seeing Via Miniature Eye Movements- A Dynamic Hypothesis for Vision [http://www.frontiersin.org/Journal/DownloadFile/1/91957/32594/1/21/fncom-06-00089_pdf]<br />
<br />
* [Apr 20] <font color="#ff0000">Ryan Zarcone, Brian Cheung, Charles Frye</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]<br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8516TCN2016-04-13T19:12:42Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 18] <font color="#ff0000">Dylan Paiton</font><br />
<br />
* [May 11] <font color="#ff0000">Karl Zipser</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
* [Apr 20] <font color="#ff0000">Ryan Zarcone, Brian Cheung, Charles Frye</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]<br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8515TCN2016-04-13T19:12:16Z<p>Dpaiton: /* Topics in Computational Neuroscience */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 18] <font color="#ff0000">Dylan Paiton</font><br />
<br />
* [May 11] <font color="#ff0000">Karl Zipser</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
* [Apr 20] <font color="#ff0000">Brian Cheung, Charles Frye, Ryan Zarcone</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]<br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8514TCN2016-04-13T19:10:55Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 11] <font color="#ff0000">Karl Zipser</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
* [Apr 20] <font color="#ff0000">Brian Cheung, Charles Frye, Ryan Zarcone</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]<br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8513TCN2016-04-13T18:59:07Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 11] <font color="#ff0000">Karl Zipser</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
* [Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8508TCN2016-04-07T16:50:37Z<p>Dpaiton: /* Topics in Computational Neuroscience */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
* [May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
* [May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
* [May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
* [Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
* [Feb 10] Cancelled - EECS Colloquium <br />
<br />
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
* [Nov 26] Thanksgiving break<br />
<br />
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8507TCN2016-04-07T16:42:43Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]<br />
<br />
[Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]<br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
[Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8504Seminars2016-04-03T23:12:00Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
'''Mar 30, 2016'''<br />
* Tony Zador HWNI job talk at 12:00<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''April 13, 2016'''<br />
* Bence Olveczky - HWNI job talk at 12:00<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title: Sparse Coding ASIC Chips for Feature Extraction and Classification<br />
* Abstract: Hardware-based computer vision accelerators will be an essential part of future mobile and autonomous devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to the underlying algorithms, which is an advantage over software-based solutions and general-purpose hardware. In this talk, I will present three application-specific integrated circuit (ASIC) chips that implement the sparse and independent local network (SAILnet) algorithm and the locally competitive algorithm (LCA) for feature extraction and classification. Two of the chips were designed using an array of leaky integrate-and-fire neurons. Sparse activations of the neurons make possible an efficient grid-ring architecture to deliver an image processing throughput of 1 G pixel/s using only 200 mW. The third chip was designed using a convolution approach. Sparsity is again an important factor that enabled the use of sparse convolvers to achieve an effective performance of 900 G operations/s using less than 150 mW.<br />
<br />
<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=Past_TCN_Papers&diff=8503Past TCN Papers2016-03-31T00:08:45Z<p>Dpaiton: /* Syllabus (Topics and Readings) */</p>
<hr />
<div>=== Syllabus (Topics and Readings) ===<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== Summer 2010 ===<br />
<br />
* [June 10] <br />
** Jarzynski, Nonequilibrium work relations: foundations and applications. [http://tinyurl.com/2fxyowa]<br />
** Crooks, Nonequilibrium Measurements of Free Energy Differences for Microscopically Reversible Markovian Systems. [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.63.4984&rep=rep1&type=pdf]<br />
** Crooks, Beyond Boltzmann-Gibbs statistics: Maximum entropy hyperensembles out of equilibrium [http://www.google.com/url?sa=t&source=web&cd=1&ved=0CBIQFjAA&url=http%3A%2F%2Fthreeplusone.com%2Fpubs%2Ffulltext%2FCrooks2007b.pdf&ei=G4QZTJKNKoLSNYCj8PcI&usg=AFQjCNEPftRKxOfq3hMWSCxpOQeTyZErZA&sig2=2xhgXNLHQQuWcN82MJBU_g]<br />
<br />
* [May 20] Truccolo et al., Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes [http://www.nature.com/neuro/journal/v13/n1/abs/nn.2455.html/]<br />
<br />
=== Spring 2010 ===<br />
<br />
* [May 13] A review of active learning (no paper).<br />
<br />
* [Apr 29] Ranzato and Hinton, Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines [http://www.cs.toronto.edu/~ranzato/publications/ranzato_cvpr2010.pdf]<br />
<br />
* [Apr 22] Boureau et al., Learning Mid-Level Features For Recognition [http://yann.lecun.com/exdb/publis/pdf/boureau-cvpr-10.pdf]<br />
<br />
* [Apr 15] Neghaban et al., A unified framework for the analysis of regularized $M$-estimators.[http://www.eecs.berkeley.edu/~sahand_n/highdimrates.pdf]<br />
<br />
* [Apr 6] Lucke et al., Occlusive components analysis. [http://www.fias.uni-frankfurt.de/~luecke/Publications/LuckeEtAl2009.pdf]<br />
<br />
* [Mar 25] Bießmann et al., Decision-related activity in sensory neurons reflects more than a neuron's causal effect.[http://www.springerlink.com/content/e1425487365v2227]<br />
<br />
* [Mar 18] Itskov and Abbott, Pattern Capacity of a Perceptron for Sparse Discrimination. [http://neurotheory.columbia.edu/~larry/ItskovPRL08.pdf]<br />
<br />
* [Mar 11] Nienborg and Cumming, Decision-related activity in sensory neurons reflects more than a neuron's causal effect.[http://www.nature.com/nature/journal/v459/n7243/full/nature07821.html]<br />
<br />
* [Feb 18] Masse et al., Olfactory Information Processing in Drosophila. [http://download.cell.com/current-biology/pdf/PIIS0960982209013013.pdf]<br />
<br />
* [Feb 11] Yaghoobi et al., Dictionary Learning for Sparse Approximations with the Majorization Method. [http://www.see.ed.ac.uk/%7Es0574225/YBD_DLMM_TSP.pdf]<br />
<br />
* [Feb 4] Ecker et al., Decorrelated Neuronal Firing in Cortical Microcircuits. [http://www.sciencemag.org/cgi/content/abstract/327/5965/584]<br />
* [Feb 4] Renart et al., The Asynchronous State in Cortical Circuits. [http://www.sciencemag.org/cgi/content/abstract/327/5965/587]<br />
<br />
* [Jan 28] Recht et al., Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization. [http://pages.cs.wisc.edu/~brecht/papers/07.rfp.lowrank.pdf]<br />
<br />
=== Fall 2009 ===<br />
<br />
* [Dec 3] Kolgin et al., Frequency of gamma oscillations routes flow of information in the hippocampus. [http://www.nature.com/nature/journal/v462/n7271/abs/nature08573.html]<br />
<br />
* [Nov 17] Graham, Chandler, Field. Can the theory of ‘‘whitening’’ explain the center-surround properties of retinal ganglion cell receptive fields? [http://math.dartmouth.edu/~dgraham/dg/rgc-paper.pdf]<br />
<br />
* [Nov 12] Lighthill and AI community debate, 1973. [http://www.aiai.ed.ac.uk/events/lighthill1973/]<br />
<br />
* [Oct 29, Nov 5] George, Hawkins, Towards a Mathematical Theory of Cortical Micro-circuits. [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000532]<br />
<br />
* [Oct 22]: Berkes et al., A Structured Model of Video Reproduces Primary Visual Cortical Organisation.[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000495]<br />
<br />
* [Oct 15]: Petreanu et al., The subcellular organization of neocortical excitatory connections. [http://www.nature.com/nature/journal/v457/n7233/pdf/nature07709.pdf]<br />
<br />
* [Oct 15]: Arenz et al. The contribution of single synapses to sensory representation in vivo. [http://www.sciencemag.org/cgi/content/full/321/5891/977]<br />
<br />
* [Oct 1,8]: Kobilarov et al., Lie Group Integrators for Animation and Control of Vehicles.[http://www.geometry.caltech.edu/pubs/KCD09.pdf]<br />
<br />
==== Last papers covered ====<br />
<br />
<big>&#10004;</big> indicates papers we've read. The others are suggestions for future readings.<br />
<br />
* <big>&#10004;</big> Tutorial on Conjugate Gradient - no paper but ''An Introduction to the Conjugate Gradient Method Without the Agonizing Pain'' by Jonathan Richard Shewchuk might be useful: [http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf link] - Presenter: <font color="#ff0000">Chris Hillar</font><br />
<br />
* <big>&#10004;</big> Tutorial on Natural Gradient - no paper but ''Amari. Natural gradient works efficiently in learning. Neural Computation'', Amari 1998 might be useful: [http://www.mitpressjournals.org/doi/abs/10.1162/089976698300017746 link] - Presenter: <font color="#ff0000">Jascha</font><br />
<br />
* <big>&#10004;</big> ''Distilling Free-Form Natural Laws from Experimental Data'', Michael Schmidt1 and Hod Lipson 2009. Science 324(59233): [http://sciencemag.org/cgi/content/abstract/324/5923/81 link] - Presenter: <font color="#ff0000">Daniel</font><br />
<br />
* <big>&#10004;</big> ''Learning Optical Flow'', Deqing Sun, Stefan Roth, J. P. Lewis, and Michael Black. EECV 2008: [http://www.gris.informatik.tu-darmstadt.de/~sroth/pubs/eccv2008.pdf link] - Presenter: <font color="#ff0000">Jack</font><br />
<br />
* <big>&#10004;</big> ''Decision-theoretic saliency: computational principles, biological plausibility, and implications for neurophysiology and psychophysics'', Gao D, Vasconcelos N. Neural Comput. 2009 Jan;21(1):239-71.: [http://www.ncbi.nlm.nih.gov/pubmed/19210172?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum link] - Presenter: <font color="#ff0000">Daniel</font><br />
<br />
* <big>&#10004;</big> ''Temporal interactions between cortical rhythms'', Roopun AK, Kramer MA, Carracedo LM, Kaiser M, Davies CH, Traub RD, Kopell NJ and Whittington MA. (2008) Front. Neurosci. 2,2:145-154. doi:10.3389/neuro.01.034.2008: [http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2622758 link] - Presenter: <font color="#ff0000">Kilian</font><br />
<br />
* <big>&#10004;</big> ''Recognition of natural scenes from global properties: seeing the forest without representing the trees'', Greene, M.R. & Oliva, A. 2009 Cognitive Psychology Vol. 58(2), pp. 137-76 [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WCR-4TB9P8G-1&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=3033c96c7f8db6b8a6f6761be811ad79 link] - Presenter: <font color="#ff0000">Paul Ivanov</font><br />
<br />
* <big>&#10004;</big> ''Cogito componentiter ergo sum'', Lars Kai Hansen and Ling Feng. Lecture Notes in Computer Science, 2006: [http://www.springerlink.com/index/j456687g40246n57.pdf link] - Presenter: <font color="#ff0000">?</font><br />
<br />
* <big>&#10004;</big> Tutorial on representation theory and invariance - no paper but ''A novel set of rotationally and translationally invariant features for images based on the non-commutative bispectrum'' by Risi Kondor might be useful: [http://arxiv.org/abs/cs.CV/0701127 link] - Presenter: <font color="#ff0000">Chris Hillar</font><br />
<br />
* <big>&#10004;</big> ''If I Were You: Perceptual Illusion of Body Swapping'', Petkova VI, Ehrsson HH 2008. PLoS ONE 3(12): e3832 doi:10.1371/journal.pone.0003832 [http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0003832 link] - Presenter: <font color="#ff0000">Jascha</font><br />
<br />
* <big>&#10004;</big> ''Multisensory integration in macaque visual cortex depends on cue reliability'', Morgan ML, Deangelis GC, Angelaki DE., Neuron. 2008 Aug 28;59(4):662-73. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSS-4T9MB77-H&_user=4420&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000059607&_version=1&_urlVersion=0&_userid=4420&md5=af04fa45c08906f07d4f7d270335cdc6 link] - Presenter: <font color="#ff0000">Rama Natarajan</font><br />
<br />
* <big>&#10004;</big> ''Design of Linear Equalizers Optimized for the Structural Similarity Index'', S. Channappayya, Alan Bovik, C. Caramanis, R. Heath Jr; IEEE Transactions on Image Processing, Vol 17 NO 6 June 2008 [http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=4483677&isnumber=4515964&punumber=83&k2dockey=4483677@ieeejrns&query=%28%28bovik%29%3Cin%3Emetadata%29&pos=4&access=no link] - Presenter: <font color="#ff0000">Jimmy</font><br />
<br />
* <big>&#10004;</big> ''Kalman filtered compressed sensing'', Namrata Vaswani, ICIP 2008 [http://arxiv.org/abs/0804.0819 link] - Presenter: <font color="#ff0000">Jack</font><br />
<br />
* <big>&#10004;</big> ''Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades'', Shlomit Yuval-Greenberg, Orr Tomer, Alon S. Keren, Israel Nelken and Leon Y. Deouell, Neuron, 58, pp. 429–441, 2008. [http://www.neuron.org/content/article/abstract?uid=PIIS0896627308003012 link] - Presenter: <font color="#ff0000">Paul and Tim</font><br />
<br />
* <big>&#10004;</big> ''Internally Generated Reactivation of Single Neurons in Human Hippocampus During Free Recall'', Hagar Gelbard-Sagiv, Roy Mukamel, Michal Harel, Rafael Malach, Itzhak Fried, Science, Published Online September 4, 2008. [http://www.sciencemag.org/cgi/content/short/1164685 link] - Presenter: <font color="#ff0000">Will</font><br />
<br />
* <big>&#10004;</big> ''Saliency Detection: A Spectral Residual Approach'', Xiaodi Hou and Liqing Zhang, in Proc. of CVPR '07, pp. 1-8, 2007. [http://bcmi.sjtu.edu.cn/~houxiaodi/papers/CVPR07.pdf link] - Presenter: <font color="#ff0000">Chetan</font><br />
<br />
* <big>&#10004;</big> ''The information bottleneck method'', N. Tishby, F.C. Pereira and W. Bialek, in Proceedings of the 37th Annual Allerton Conference on Communication, Control and Computing, B. Hajek and R.S. Sreenivas eds, pp. 368-377 (University of Illinois, 1999). [http://www.princeton.edu/~wbialek/our_papers/tishby+al_99.pdf link] - Presenter: <font color="#ff0000">Pierre</font><br />
<br />
* <big>&#10004;</big> ''Evolutionary expansion and anatomical specialization of synapse proteome complexity'', R. D. Emes et al., Nature Neuroscience, vol. 11, no. 7, pp. 799-806, June 2008. [http://www.nature.com/neuro/journal/v11/n7/full/nn.2135.html link] - Presenter: <font color="#ff0000">Jeff</font><br />
<br />
* <big>&#10004;</big> ''Stability of the fittest: organizing learning through retroaxonal signals'', Ken Harris, Trends Neurosci, 31(3), pp. 130-136, 2008. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T0V-4RS3TBR-1&_user=4420&_coverDate=03%2F31%2F2008&_alid=770804385&_rdoc=1&_fmt=high&_orig=search&_cdi=4872&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000059607&_version=1&_urlVersion=0&_userid=4420&md5=38891fcb62351e3bfa79f00214a3dbfb link] - Presenter: <font color="#ff0000">Will</font><br />
<br />
* <big>&#10004;</big> ''Object recognition with cortex-like mechanisms'', T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber and T. Poggio, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (3), pp. 411-426, 2007. [http://web.mit.edu/serre/www/publications/Serre_etal_PAMI07.pdf link]- Presenter: <font color="#ff0000">Jack</font><br />
<br />
* <big>&#10004;</big> ''Nonlinear extraction of 'Independent Components' of elliptically symmetric densities using radial Gaussianization'', S. Lyu and E. P. Simoncelli, Computer Science Technical Report TR2008-911, Apr 2008. [http://www.cns.nyu.edu/pub/lcv/lyu08a.pdf link] - Presenter: <font color="#ff0000">Bruno</font><br />
<br />
* <big>&#10004;</big> ''Relating neural dynamics to neural coding'', G. Bard Ermentrout, Roberto F. Galàn and Nathaniel N. Urban, Physical Review Letters, vol. 99, no. 24, December 2007. [http://www.andrew.cmu.edu/user/rfgalan/pdf/Ermentrout_etal_PRL07.pdf link] - Presenter: <font color="#ff0000">Ariel</font><br />
<br />
* <big>&#10004;</big> ''Spatio-temporal correlations and visual signaling in a complete neuronal population'', Jonathan W. Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, Eero P. Simoncelli, Nature (to appear), 2008. - Presenter: <font color="#ff0000">Jonathon Shlens</font><br />
<br />
* <big>&#10004;</big> ''Learning Compressed Sensing'', Yair Weiss and Hyun Sung Chang and William T. Freeman, Allerton 2007. [http://www.cs.huji.ac.il/~yweiss/allerton-final.pdf link] - Presenter: <font color="#ff0000">Pierre</font><br />
<br />
* <big>&#10004;</big> ''Sparse and shift-invariant feature extraction from non-negative data'', P. Smaragdis, B. Raj, and M.V. Shashanka, In proceedings IEEE International Conference on Audio and Speech Signal Processing, April 2008. [http://web.media.mit.edu/~paris/Paris_Smaragdis/Paris_Smaragdis_web_page_files/smaragdis-icassp2008.pdf link] - Presenter: <font color="#ff0000">Vivienne</font><br />
<br />
* <big>&#10004;</big> ''Context and hierarchy in a probabilistic image model'', Y. Jin and S. Geman, CVPR (2) 2006, pp. 2145-2152. [http://www.dam.brown.edu/people/geman/Homepage/Compositionality%20in%20computer%20vision/cvpr06_final.pdf link] - Presenter: <font color="#ff0000">Amir</font><br />
<br />
* <big>&#10004;</big> ''Entrainment of neuronal oscillations as a mechanism of attentional selection'', Peter Lakatos, George Karmos, Ashesh D. Mehta, Istvan Ulbert, Charles E. Schroeder, Science, vol. 320, no. 5872, pp. 110-113, April 2008. [http://www.sciencemag.org/cgi/content/full/320/5872/110 link] - Presenter: <font color="#ff0000">Gianluca</font><br />
<br />
* <big>&#10004;</big> ''The missing memristor found'', Dmitri B. Strukov, Gregory S. Snider, Duncan R. Stewart, R. Stanley Williams, Nature, vol. 453, pp. 80-83, May 2008. - Presenter: <font color="#ff0000">Chris</font><br />
<br />
* <big>&#10004;</big> ''80 million tiny images: a large dataset for non-parametric object and scene recognition'', A. Torralba, R. Fergus, W. T. Freeman, submitted to IEEE PAMI, October 2007. [http://people.csail.mit.edu/torralba/tmp/tiny.pdf link] - Presenter: <font color="#ff0000">Jascha</font><br />
<br />
* <big>&#10004;</big> ''Decoupling through Synchrony in Neuronal Circuits with Propagation Delays'', E. V. Lubenov and A. G. Siapas. Neuron, vol. 58, 2008, pp. 118-131. [http://www.neuron.org/content/article/abstract?uid=PIIS0896627308001281 link] - Presenter: <font color="#ff0000">Amir</font><br />
<br />
* <big>&#10004;</big> ''The role of learning in 3-D form perception'', P. Sinha and T. Poggio. Nature, vol. 384, no 6608, 1996, pp. 460-463. [http://web.mit.edu/bcs/sinha/papers/sinha_poggio_1996.pdf link] - Presenter: <font color="#ff0000">Charles </font><br />
<br />
* <big>&#10004;</big> ''Sparse Feature Learning for Deep Belief Networks'', M. Ranzato, Y. Boureau, Y. LeCun. Advances in Neural Information Processing Systems 20 (NIPS 2007). [http://www.cs.nyu.edu/~ranzato/publications/ranzato-nips07.pdf link] - Presenter: <font color="#ff0000">Jimmy</font><br />
<br />
* <big>&#10004;</big> ''Phase-of-Firing Coding of Natural Visual Stimuli in Primary Visual Cortex'', Marcelo A. Montemurro, Malte J. Rasch, Yusuke Murayama, Nikos K. Logothetis, and Stefano Panzeri. Current Biology, vol. 18, no. 5, Mar 2008, pp. 375-380. [http://www.current-biology.com/content/article/abstract?uid=PIIS0960982208001681 link] - Presenter: <font color="#ff0000"> Kilian </font><br />
<br />
* <big>&#10004;</big> ''Multisensory auditory–visual interactions during early sensory processing in humans: a high-density electrical mapping study'', Sophie Molholm, Walter Ritter, Micah M. Murray, Daniel C.Javitt, Charles E. Schroeder and John J. Foxe. Cognitive Brain Research, vol. 14, no. 1, Jun 2002, pp. 115-128. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6SYV-453NT51-3&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=2411980aadd64b5e82e152d090c8cdbd link] - Presenter: <font color="#ff0000"> Gianluca </font><br />
<br />
* <big>&#10004;</big> ''The sleep switch: hypothalamic control of sleep and wakefulness'', Clifford B. Saper, Thomas C. Chou and Thomas E. Scammell, Trends in Neurosciences, Vol. 24, No. 12, Dec 2001. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T0V-44FBS07-11&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=54f2cf21b3a8f2dd7effb8732a331506 link] - Presenter: <font color="#ff0000"> Will </font><br />
<br />
* <big>&#10004;</big> ''Sparse time-frequency representations'', T. J. Gardner and M. O. Magnasco, PNAS, vol. 103, no. 16, pp. 6094-6099, 2006. [http://www.pnas.org/cgi/content/abstract/103/16/6094 link] - Presenter: <font color="#ff0000"> Marcelo Magnasco</font><br />
<br />
* <big>&#10004;</big> ''Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication'', H. Jaeger and H. Haas, Science, Vol. 304, Issue 5667, pp. 78-80, 2004. [http://www.sciencemag.org/cgi/content/abstract/304/5667/78 link] - Presenter: <font color="#ff0000">Jascha</font><br />
<br />
* <big>&#10004;</big> ''Does spatial invariance result from insensitivity to change?'', F. A. A. Kingdom, D. J. Field and A. Olmos, Journal of Vision, 7(14):11, 1-13, Dec. 2007. [http://journalofvision.org/7/14/11/ link] - Presenter: <font color="#ff0000"> Bruno Olshausen </font><br />
<br />
* <big>&#10004;</big> ''How Behavioral Constraints May Determine Optimal Sensory Representations'', Emilio Salinas, PLoS Biology, 6:12, Dec. 2006. [http://biology.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pbio.0040387 link] - Presenter: <font color="#ff0000"> Lavi Secundo </font><br />
<br />
* <big>&#10004;</big> Convexity, disparity, and depth perception: internalization of natural scene statistics. Johannes Burge, Charless C. Fowlkes, Martin S. Banks. DRAFT. Presenter: <font color="#ff0000"> Johannes Burge </font><br />
<br />
* <big>&#10004;</big> The K-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation. IEEE Trans. On Signal Processing, Vol. 54, no. 11, pp. 4311-4322, November 2006. [http://www.cs.technion.ac.il/~elad/publications/journals/2004/32_KSVD_IEEE_TSP.pdf link] Presenter: <font color="#ff0000"> Gianluca Monaci </font><br />
<br />
* <big>&#10004;</big> Image Denoising with Nonparametric Hidden Markov Trees. J. Kivinen, E. Sudderth, and M. Jordan. ICIP 2007. [http://ssg.mit.edu/~esuddert/papers/icip07.pdf link] Presenter: <font color="#ff0000"> Jack Culpepper </font><br />
<br />
* <big>&#10004;</big> Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex. Mukamel R, Gelbard H, Arieli A, Hasson U, Fried I, Malach R. Science. 2005 Aug 5;309(5736):951-4. [http://www.sciencemag.org/cgi/content/abstract/309/5736/951?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&fulltext=Coupling+between+neuronal+firing%2C+field+potentials%2C+and+FMRI+in+human+auditory+cortex.&searchid=1&FIRSTINDEX=0&resourcetype=HWCIT link] Presenter: <font color="#ff0000"> Tim Blanche </font><br />
<br />
* <big>&#10004;</big> The design and use of steerable filters. W. T. Freeman and E. H. Adelson, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 13, no. 9, pp. 891 - 906, September, 1991. [http://people.csail.mit.edu/billf/papers/steerpaper91FreemanAdelson.pdf link]Presenter: <font color="#ff0000"> Jimmy Wang </font><br />
<br />
* <big>&#10004;</big> Problem reduction, renormalization, and memory. Commun. Appl. Math. Comput. Sci. 1 1-27. Chorin, Alexandre J. and Stinis, Panagiotis (2006). Presenter: <font color="#ff0000"> Amir and Fritz </font><br />
<br />
* <big>&#10004;</big> Learning Lie Groups of Visual Invariance. Xu Miao and Rajesh P.N. Rao. Neural Computation 19,2665-2693 (2007). Presenter: <font color="#ff0000"> Pierre Garrigues </font><br />
<br />
* <big>&#10004;</big> Correlation between neural spike trains increases with firing rate. Jaime de la Rocha, Brent Doiron, Eric Shea-Brown, Kresimir Josic & Alex Reyes. Nature 448, 802-806 (16 August 2007) [http://www.nature.com/nature/journal/v448/n7155/abs/nature06028.html link]. Presenter: <font color="#ff0000"> Chris Rozell </font><br />
<br />
* <big>&#10004;</big> Adaptation to Stimulus Contrast and Correlations during Natural Visual Stimulation. Nicholas A Lesica, Jianzhong Jin, Chong Weng, Chun-I Yeh, Daniel A Butts, Garrett B Stanley, Jose-Manuel Alonso. Neuron 2007 vol. 55 pp. 479-91. Presenter: <font color="#ff0000"> Tim Blanche </font><br />
<br />
* <big>&#10004;</big> Quest for a Stochastic Grammar of Images. Song-Chun Zhu and David Mumford. To appear in Foundations and Trends in Computer Graphics and Vision, 2007. [http://www.stat.ucla.edu/~sczhu/papers/Grammar_quest.pdf link]. Presenter: <font color="#ff0000"> Amir </font><br />
<br />
* <big>&#10004;</big> Slow feature analysis yields a rich repertoire of complex cell properties. Berkes, P. and Wiskott, L. (2005). Journal of Vision, 5(6), 579-602. [http://journalofvision.org/5/6/9/article.aspx link]. Presenter: <font color="#ff0000"> Charles Cadieu </font><br />
<br />
* <big>&#10004;</big> Unsupervised learning of image transformations. Memisevic, R. and Hinton, G. E. Computer Vision and Pattern Recognition 2007 [http://www.cs.toronto.edu/~rfm/pubs/cvpr07.pdf link]. Presenter: <font color="#ff0000"> Jack Culpepper </font><br />
<br />
* <big>&#10004;</big> The thalamus as a monitor of motor outputs. R. W. Guillery and S. M. Sherman. Phil. Trans. R. Soc. Lond. B (2002) 357, 1809-1821. Presenter: <font color="#ff0000"> Jascha Sohl-Dickstein </font><br />
<br />
* <big>&#10004;</big> Recovering Intrinsic Images from a Single Image. Tappen, M. F., Freeman, W. T., and Adelson, E. H.. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9): 1459 - 1472, (2005). [http://web.mit.edu/persci/people/adelson/documents/Tappen_PAMI_27-9.pdf link] Presenter: <font color="#ff0000"> Chetan Nandakumar </font><br />
<br />
* <big>&#10004;</big> N. Ay, D. Polani. Information Flows in Causal Networks. Advances in Complex Systems, submitted. Santa Fe Institute Working Paper 06-05-014. [http://www.santafe.edu/research/publications/workingpapers/06-05-014.pdf link] Presenter: <font color="#ff0000"> Joe Makin </font><br />
<br />
* <big>&#10004;</big> Der R., Hesse F., Liebscher R.: Contingent robot behavior from self-referential dynamical systems. Autonomous robots submitted. [http://www.informatik.uni-leipzig.de/~der/Veroeff/autorob.pdf link] Presenter: <font color="#ff0000"> Fritz Sommer </font><br />
<br />
* <big>&#10004;</big> Z Wang, A C Bovik and E P Simoncelli. Structural approaches to image quality assessment. Chapter 8.3 in Handbook of Image and Video Processing, 2nd Edition, ed. Alan Bovik, Academic Press, 2005. [http://www.cns.nyu.edu/pub/lcv/wang05c-preprint.pdf link] Presenter: <font color="#ff0000"> Jimmy Wang </font><br />
<br />
* <big>&#10004;</big> O. Ben-Shahar and S.W. Zucker. Geometrical Computations Explain Projection Patterns of Long Range Horizontal Connections in Visual Cortex, in Neural Computation, 16(3): 445-476, 2004. [http://www.cs.yale.edu/~shahar/Publications/NC2004.pdf link] Presenter: <font color="#ff0000"> Pierre Garrigues </font><br />
<br />
* <big>&#10004;</big> Zhaoping L. (2005) Border Ownership from Intracortical Interaction in Visual Area V2 , in NEURON, Vol. 47, 143-153 [http://www.gatsby.ucl.ac.uk/~zhaoping/prints/ZhaopingNEURON2005JournalVersion.pdf link] Presenter: <font color="#ff0000"> Paul King </font><br />
<br />
* <big>&#10004;</big> Nakayama, K. , He, Z.J. and Shimojo, S. Visual surface representation: a critical link between lower-level and higher level vision. In Kosslyn, S.M. and Osherson, D.N. Vision. In Invitation to Cognitive Science. M.I.T. Press, p. 1-70, 1995 [http://visionlab.harvard.edu/Members/Ken/Ken%20papers%20for%20web%20page/077NKHeShimojoMIT1995b.pdf link] Presenter: <font color="#ff0000"> Jascha Sohl-Dickstein </font><br />
<br />
* <big>&#10004;</big> Sinha, P., Balas, B.J., Ostrovsky, Y., & Russell, R."Face Recognition by Humans: 19 results all computer vision researchers should know about." Proceedings of the IEEE. Vol. 94, No. 11, 1948-1962 (2006) [web.mit.edu/bcs/sinha/papers/20Results_2005.pdf link] Presenter: <font color="#ff0000"> Chetan Nandakumar </font><br />
<br />
* <big>&#10004;</big> Chandler DM, and Field DJ. "Estimates of the Information Content and Dimensionality of Natural Scenes from Proximity Distributions." Journal of the Optical Society of America, 2006 (in press) [http://redwood.psych.cornell.edu/manuscripts/chandler_field_xentropy_2006.pdf link] Presenter: <font color="#ff0000"> Paul Ivanov </font><br />
<br />
* <big>&#10004;</big> Z. Tu, X. Chen, A.L. Yuille, and S.C. Zhu. "Image Parsing: Unifying Segmentation, Detection, and Recognition". Proceedings of ICCV 2003. [http://www.stat.ucla.edu/~yuille/pubs/impaired/iccv_parsing.pdf link] Presenter: <font color="#ff0000"> Jimmy Wang </font><br />
<br />
* <big>&#10004;</big> E. Schneidman, M. Berry, R. Segev and W. Bialek. "Weak pairwise correlations imply strongly correlated network states in a neural population". Nature 440, 1007-1012 (20 April 2006) [http://www.nature.com/nature/journal/v440/n7087/full/nature04701.html link] Presenter: <font color="#ff0000"> Pierre Garrigues </font><br />
<br />
* <big>&#10004;</big> S. Ullman, S. Soloviev. "Computation of Pattern Invariance in Brain-like Structures". Neural Networks 1999. [http://www.wisdom.weizmann.ac.il/~shimon/papers/NN12_99.pdf link] Presenter: <font color="#ff0000"> Charles Cadieu </font><br />
<br />
* <big>&#10004;</big> P. Bak, C. Tang, and K. Wiesenfeld. "Self-Organized Critically: An Explanation of 1/f Noise". Physical Review Letters, July 1987. Presenter: <font color="#ff0000"> Kilian Koepsell </font><br />
<br />
* <big>&#10004;</big> A. Hyvarinen. "Estimation of Non-Normalized Statistical Models by Score Matching". Journal of Machine Learning Research 6 (2005) 695-709. Presenter: <font color="#ff0000"> Jascha Sohl-Dickstein </font><br />
<br />
* <big>&#10004;</big> M. Ranzato, C.S. Poultney, S. Chopra and Y. LeCun. "Efficient Learning of Sparse Overcomplete Representations with an Energy-Based Model". Advances in Neural Information Processing Systems 19, in Scholkopf et al. (eds), MIT Press, Cambridge, MA, 2007. Presenter: <font color="#ff0000"> Pierre Garrigues </font><br />
<br />
* <big>&#10004;</big> Liam Paninski, Jonathan Pillow and Eero Simoncelli. Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. Neural Computation, 2004. Presenter: <font color="#ff0000"> Amir </font><br />
<br />
* <big>&#10004;</big> Randall C. O'Reilly. Biologically Based Computational Models of High-Level Cognition. Science Oct. 2006. Presenter: <font color="#ff0000"> Charles Cadieu </font><br />
<br />
* <big>&#10004;</big> Todorov, E. and Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5, 1226-1235, 2002.<br />
<br />
* <big>&#10004;</big> Asari, H., Pearlmutter, B. A. and Zador, A. M. Sparse Representations for the Cocktail Party Problem. The Journal of Neuroscience, July 12, 2006.<br />
<br />
* <big>&#10004;</big> Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.<br />
<br />
* <big>&#10004;</big> P. Dayan, Image, Frames, and Connectionist Hierarchies. Neural Computation 18, 2293-2319 (2006)<br />
<br />
* <big>&#10004;</big> T. Blaschke, P. Berkes and L. Wiskott, What Is the Relation Between Slow Feature Analysis and Independent Component Analysis? Neural Computation 18, 2495-2508 (2006)<br />
<br />
* <big>&#10004;</big> T.J. Gardner, and M.O. Magnasco, Sparse time-frequency representations PNAS 103: 6094-6099, April 18, 2006 [http://www.pnas.org/cgi/reprint/103/16/6094.pdf paper]<br />
<br />
* <big>&#10004;</big> B.L. McNaughton, F.P. Battaglia, O. Jensen, E.I. Moser and M.B. Moser, Path integration and the neural basis of the 'cognitive map'. Nature Reviews Neuroscience 7, 663-678 (August 2006) [http://www.nature.com/nrn/journal/v7/n8/full/nrn1932.html paper]<br />
<br />
* <big>&#10004;</big> Karklin, Lewicki. A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals. Neural Computation. 2005;17:397-423. [http://www.mitpressjournals.org/doi/abs/10.1162/0899766053011474 paper] [http://video.google.com/videoplay?docid=-5115453018099427284&q=msri MSRI talk]<br />
<br />
* <big>&#10004;</big> Perspectives: Jose-Manuel Alonso, Neurons Find Strength Through Synchrony in the Brain, Science, 16 June 2006, Vol. 312. no. 5780, pp. 1604 - 1605 [http://www.sciencemag.org/cgi/content/short/312/5780/1604 link]<br />
<br />
* <big>&#10004;</big> Randy M. Bruno and Bert Sakmann, Cortex Is Driven by Weak but Synchronously Active Thalamocortical Synapses, Science, 16 June 2006, Vol. 312. no. 5780, pp. 1622 - 1627 [http://www.sciencemag.org/cgi/content/short/312/5780/1622 link]<br />
<br />
* <big>&#10004;</big> David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature 440, 680-683 (30 March 2006) [http://www.nature.com/nature/journal/v440/n7084/pdf/nature04587.pdf link]<br />
<br />
* <big>&#10004;</big> Guetig, Sompolinsky. The Tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience 2006; 9:420-428. [http://www.nature.com/neuro/journal/v9/n3/pdf/nn1643.pdf link]<br />
<br />
* <big>&#10004;</big> E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]<br />
<br />
: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]<br />
<br />
* <big>&#10004;</big> D. Y. Tsao, W. A. Freiwald, R. B. H. Tootell, M. S. Livingstone, A Cortical Region Consisting Entirely of Face-Selective Cells, Science 3 February 2006; Vol. 311. no. 5761, pp. 670 - 674. [http://www.sciencemag.org/cgi/content/full/311/5761/670 link]<br />
<br />
: Perspectives: Kanwisher, "What's in a Face?". [http://www.sciencemag.org/cgi/content/full/311/5761/617 link]<br />
<br />
* <big>&#10004;</big> Harris Nover, Charles H. Anderson, and Gregory C. DeAngelis. A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance. J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/content/full/25/43/10049?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&author1=anderson&searchid=1&FIRSTINDEX=0&resourcetype=HWCIT link]<br />
<br />
* <big>&#10004;</big> Randy M. Bruno and Bert Sakmann, Cortex Is Driven by Weak but Synchronously Active Thalamocortical Synapses, Science, 16 June 2006, Vol. 312. no. 5780, pp. 1622 - 1627 [http://www.sciencemag.org/cgi/content/short/312/5780/1622 link]<br />
<br />
: Perspectives: Jose-Manuel Alonso, Neurons Find Strength Through Synchrony in the Brain, Science, 16 June 2006, Vol. 312. no. 5780, pp. 1604 - 1605 [http://www.sciencemag.org/cgi/content/short/312/5780/1604 link]<br />
<br />
* <big>&#10004;</big> C Whitney & P Cornelissen (2005). Letter-position encoding and dyslexia. Journal of Research in Reading 28, 274-301. [http://www.cs.umd.edu/~shankar/cwhitney/Papers/JRR.pdf paper]<br />
<br />
==== Early Work ====<br />
<br />
* <big>&#10004;</big> K. A. C. Martin, The Pope and grandmother−a frog's-eye view of theory, Nature Neuroscience 3, 1169 (2000) [http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1169.html link]<br />
<br />
* <big>&#10004;</big> Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" [http://jerome.lettvin.info/WhatTheFrogsEyeTellsTheFrogsBrain.pdf link]<br />
<br />
* <big>&#10004;</big> Barlow HB (1972) Single Neurons and Sensation: A neuron doctrine for perceptual psychology. Perception 1, 371-394. [http://redwood.berkeley.edu/~amir/pdf/Barlow1972.pdf link]<br />
<br />
* Marvin Minsky, Steps Toward Artificial Intelligence [http://portal.acm.org/citation.cfm?id=216408.216442&coll=GUIDE&dl=GUIDE link]<br />
<br />
* McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” (1943) [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104377 link]<br />
<br />
* Marr, selection from Vision; Artificial Intelligence—a personal view, by David Marr [http://scholar.google.com/url?sa=U&q=https://dspace.mit.edu/handle/1721.1/5776%3Fmode%3Dsimple link]<br />
<br />
* Readings from Dartmouth Conf. 1956 proceedings<br />
* Rosenblatt, Frank (1962). Principles of neurodynamics. New York: Spartan. Cf. Rumelhart, D.E., J. L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing vol. 1&2. Cambridge: MIT. [http://scholar.google.com/scholar?hl=en&lr=&q=rosenblatt+principles+of+neurodynamics&btnG=Search link]<br />
<br />
* "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint" (with H. Dreyfus),Daedulus, Winter 1988 [http://portal.acm.org/citation.cfm?id=63323.66521 link]<br />
<br />
==== Coding ====<br />
<br />
* <big>&#10004;</big> Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004. [http://dx.doi.org/10.1016/j.plrev.2004.06.001 link]<br />
<br />
* <big>&#10004;</big> Pouget, A, Dayan, P & Zemel, RS (2000). Information processing with population codes. Nature Reviews Neuroscience, 1 , 125-132. [http://www.nature.com/nrn/journal/v1/n2/full/nrn1100_125a_fs.html link]<br />
<br />
* <big>&#10004;</big> E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]<br />
<br />
: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures" [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]<br />
<br />
* Olshausen BA, Field DJ. Sparse Coding of Sensory Inputs. Curr Op in Neurobiology, 14: 481-487 (2004). [http://redwood.psych.cornell.edu/papers/current-opinion.pdf link]<br />
<br />
* Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103–115 (1990). [http://www.princeton.edu/~wbialek/our_papers/bialek+zee_90.pdf link]<br />
<br />
* Selection from: Spikes: Exploring the Neural Code. F Rieke, D Warland, R de Ruyter van Steveninck & W Bialek (MIT Press, Cambridge, 1997). [http://melvyl.cdlib.org/F/?func=find-b&base=CDL90&request=0-262-18174-6&find_code=020 link]<br />
<br />
* <big>&#10004;</big> H. Nover, C. H. Anderson, and G. C. DeAngelis, A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance, J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/reprint/25/43/10049 link]<br />
<br />
* <big>&#10004;</big> Ray Singh and Chris Eliasmith, Higher-Dimensional Neurons Explain the Tuning and Dynamics of Working Memory Cells, J. Neurosci. 2006;26 3667-3678 [http://www.jneurosci.org/cgi/reprint/26/14/3667 link]<br />
<br />
==== Cortical Microcircuit/Universal Cortical Algorithm ====<br />
<br />
* <big>&#10004;</big> Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press [http://redwood.berkeley.edu/~amir/pdf/Mountcastle78.pdf link]<br />
<br />
* <big>&#10004;</big> Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 [http://arjournals.annualreviews.org/doi/full/10.1146/annurev.neuro.27.070203.144152 link]<br />
<br />
* <big>&#10004;</big> Selection from: Hawkins, J., On Intelligence (Chapter 6) [http://redwood.berkeley.edu/~amir/pdf/HawkinsChap6.pdf link]<br />
<br />
* <big>&#10004;</big> Henry Markram, The Blue Brain Project, Nature Neuroscience, 7:153-160, 2006. [http://www.nature.com/nrn/journal/v7/n2/pdf/nrn1848.pdf link]<br />
<br />
* Douglas RJ, Martin KAC Whitteridge D. (1989) A canonical microcircuit for neocortex. Neural Computation 1: 480-488. [http://scholar.google.com/scholar?hl=en&lr=&q=douglas+martin+whitteridge+neural+comput+1989&btnG=Search link] (the [http://www.archive.org/details/redwood_center_inaugural_symposium_08 related talk] at the Redwood symposium)<br />
<br />
* Cross-modal plasticity in cortical development: differentiation and specification sensory cortex, by Mriganka Sur, Sarah L. Pallas and Anna W. Roe. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1694329&dopt=Citation link]<br />
<br />
* Poggio, T. and E. Bizzi. Generalization in Vision and Motor Control, Nature, Vol. 431, 768-774, 2004. [http://www.nature.com/nature/journal/v431/n7010/abs/nature03014.html link]<br />
<br />
* Marr D, "A Theory for Cerebral Neocortex", Proc Roy Soc London(B), 176, 161-234, 1970. [http://adsabs.harvard.edu/abs/1970RSPSB.176..161M link]<br />
<br />
==== Feedback, Hierarchical Organization, Generative Models ====<br />
<br />
* <big>&#10004;</big> David Mumford, Neuronal Architectures for Pattern-theoretic Problems, in "Large-Scale Neuronal Theories of the Brain", C.Koch & J.Davis, editors, MIT Press, 1994, pp.125-152. [http://redwood.berkeley.edu/~amir/pdf/Neuronal_Mumford.pdf link]<br />
<br />
* <big>&#10004;</big> RPN Rao. Bayesian inference and attention in the visual cortex. Neuroreport 16(16), 1843-1848, 2005. [http://www.cs.washington.edu/homes/rao/nreport_bayes_atten05.pdf link]<br />
<br />
* TS Lee, D Mumford Hierarchical Bayesian inference in the visual cortex, Journal of the Optical Society of America A, 2003 [http://scholar.google.com/url?sa=U&q=http://www.dam.brown.edu/people/mumford/Papers/JOSALeeMumford.pdf link]<br />
<br />
* Felleman, DJ and Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1822724&dopt=Abstract link]<br />
<br />
==== Manifold Learning ====<br />
<br />
* <big>&#10004;</big> Richard Durbin & Graeme Mitchison, A dimension reduction framework for understanding cortical maps, Nature 343, 644 - 647 (15 February 1990) [http://www.nature.com/nature/journal/v343/n6259/abs/343644a0.html link]<br />
<br />
* <big>&#10004;</big> Tal Kenet, Dmitri Bibitchkov, Misha Tsodyks, Amiram Grinvald and Amos Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425, 954-956 (30 October 2003) [http://www.nature.com/nature/journal/v425/n6961/full/nature02078.html link]<br />
<br />
* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994 [http://citeseer.ist.psu.edu/context/2930/0 link]<br />
<br />
* <big>&#10004;</big> JB Tenenbaum, V de Silva, JC Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2319 link]<br />
<br />
* <big>&#10004;</big> ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2323 link]<br />
<br />
* Nicholas V. Swindale, Doron Shoham, Amiram Grinvald, Tobias Bonhoeffer & Mark Hübener, Visual cortex maps are optimized for uniform coverage, Nature Neuroscience 3, 822 - 826 (2000) [http://www.nature.com/neuro/journal/v3/n8/full/nn0800_822.html link]<br />
<br />
Background reading on SOMs<br />
* Neural Computation and Self-Organizing Maps - An Introduction, by Helge Ritter, Thomas Martinetz, and Klaus Schulten, Addison-Wesley, New York, 1992, [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_4.pdf Kohonen's Network Model] [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_book.html Contents]<br />
<br />
==== Plasticity, Hebbian Learning ====<br />
<br />
* Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30 [http://www.ling.sinica.edu.tw/paper-Mu-ming%2520Poo-2.pdf link]<br />
<br />
* Abbott LF, Nelson SB. (2000) Synaptic plasticity: taming the beast. Nat Neurosci. 3 Suppl:1178-83. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11127835&dopt=Abstract link]<br />
<br />
* P. Foldiak, Forming sparse representations by local anti-Hebbian learning, Biological Cybernetics, vol. 64, pp. 165-170, 1990. [http://www.st-andrews.ac.uk/~pf2/FoldiakSparseBC90.pdf link]<br />
<br />
* M. Tsodyks, Spike-timing-dependent synaptic plasticity–The long road towards understanding neuronal mechanisms Trends in Neuroscience, 2002. [http://dx.doi.org/10.1016/S0166-2236(02)02294-4 link]<br />
<br />
* Saudargienne A, Porr B, and Worgotter F. How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp 16: 595–625, 2004. [http://neco.mitpress.org/cgi/content/abstract/16/3/595 link]<br />
<br />
* Seung, HS (2000) Half a century of Hebb. Nat. Neurosc. Suppl: 1166. [http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v3/n11s/full/nn1100_1166.html link] <br />
<br />
* H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063-1073 (2003). [http://hebb.mit.edu/people/seung/papers/Neuron18Dec03.pdf link]<br />
<br />
* Hinton, G. E. and Sejnowski, T. J. (1986), Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104291 link]<br />
<br />
==== Oscillations ====<br />
<br />
* Gray, CM (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron. 1999, 24(1):31-47, 111-25. Review. [http://scholar.google.com/url?sa=U&q=http://lifesci.rutgers.edu/~auerbach/Gray%2520binding.pdf link]<br />
<br />
* Neuron Special Issue on Oscillations, 1999, 24(1) [http://www.sciencedirect.com/science?_ob=IssueURL&_tockey=%23TOC%237054%231999%23999759998%23575347%23FLA%23&_auth=y&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=fcf1d593bba077aaec2c4f34adbe071d link]<br />
<br />
==== Associative Memory ====<br />
<br />
* <big>&#10004;</big> J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, 79:2554-2558 (1982) [http://www.pnas.org/cgi/content/abstract/79/8/2554 link]<br />
** David MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge Univ. Press, 2003. [http://www.inference.phy.cam.ac.uk/mackay/itprnn/ps/504.520.pdf Chapter 42 (Hopfield networks)]<br />
<br />
* <big>&#10004;</big> S. Fusi, P.J. Drew, and L.F. Abbott, Cascade Models of Synaptically Stored Memories, Neuron 2005 45: 599-611. [http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WSS-4FH5K0X-H-1&_cdi=7054&_user=4420&_orig=search&_coverDate=02%2F17%2F2005&_qd=1&_sk=999549995&view=c&wchp=dGLbVlz-zSkWz&md5=1b45b90c5fb16f945ba490ba48503de7&ie=/sdarticle.pdf link]<br />
<br />
* G. Palm. On the storage capacity of an associative memory with randomly distributed storage elements. Biol. Cybernetics. 36:19-31 (1980). [http://www.springerlink.com/link.asp?id=wp7q375127415311 link]<br />
<br />
* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994<br />
<br />
==== Models of Invariance ====<br />
<br />
* Olshausen BA, Anderson CH, Van Essen DC (1993). A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information, The Journal of Neuroscience, 13(11), 4700-4719. [http://redwood.berkeley.edu/bruno/papers/ link]<br />
<br />
* K Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics (Historical Archive), Volume 36, Issue 4, Apr 1980, Pages 193 - 202 [http://www.springerlink.com/link.asp?id=r6g5w3tt54528137 link]<br />
<br />
* <big>&#10004;</big> P. Foldiak, Learning invariance from transformation sequences, Neural Computation, vol. 3, pp. 194-200, 1991. [http://www.st-andrews.ac.uk/~pf2/FoldiakInvarianceLearningNC91.pdf link]<br />
<br />
* <big>&#10004;</big> L Wiskott. Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation. 2002;14:715-770. [http://redwood.berkeley.edu/~cadieu/pdf/WiskottSejnowski-SlowFeature.pdf link]<br />
<br />
==== Active Perception-sensorimotor loops ====<br />
<br />
* <big>&#10004;</big> D. Philipona, J.K.O'Regan & J.-P. Nadal. Is there something out there? Inferring space from sensorimotor dependencies. Neural Computation, 2003, 15, 9, 2029-2050. [http://nivea.psycho.univ-paris5.fr/Philipona/space.pdf link]<br />
<br />
* <big>&#10004;</big> Philipona, D., O Regan, J.K., Nadal, J.-P. and Coenen, O. J.-M. D. Perception of the structure of the physical world using multimodal unknown sensors and effectors. Advances in Neural Information Processing Systems, 16, 2004. [http://www.csl.sony.fr/downloads/papers/2004/philipona-04b.pdf link]<br />
<br />
* Churchland P, Ramachandran VS, Sejnowski TJ. Large-Scale Neuronal Theories of the Brain: Chapter 2, Critique of Pure Vision, 1994. [http://cnl.salk.edu/~terry/PDF/PureVision.94.pdf link]<br />
<br />
* Oregan JK, Noe A. A sensorimotor account of vision and visual consciousness, BBS (2001) 24:939-1031. [http://ist-socrates.berkeley.edu/~noe/oregan.noe.pdf link]<br />
<br />
====Birdsong====<br />
<br />
* <big>&#10004;</big> Anthony Leonardo & Michale S. Fee. Ensemble Coding of Vocal Control in Birdsong. The Journal of Neuroscience, January 19, 2005, 25(3):652-661. [http://www.jneurosci.org/cgi/reprint/25/3/652 link (Warning: 27.7 MB .pdf)]<br />
<br />
* <big>&#10004;</big> Troyer TW, Bottjer SW. Birdsong: models and mechanisms. Curr Opin Neurobiol. 2001 Dec;11(6):721-6. [http://redwood.berkeley.edu/~cadieu/pdf/TroyerBottjer_Birdsong_CurrOpinNeuro01.pdf link]<br />
<br />
==== Theories of the Ventral Stream ====<br />
<br />
* <big>&#10004;</big> Ullman, S. (1995). Sequence-seeking and counter streams: A computational model for bi-directional information flow in the visual cortex. Cerebral Cortex, 5(1) 1-11. [http://redwood.berkeley.edu/~cadieu/pdf/UllmanCerCor95.pdf link]<br />
<br />
* <big>&#10004;</big> Riesenhuber, M., and T. Poggio. Models of Object Recognition, Nature Neuroscience, 3 Supp., 1199-1204, 2000. [http://www.nature.com/neuro/journal/v3/n11s/pdf/nn1100_1199.pdf link]<br />
<br />
* Riesenhuber, M. and T. Poggio. How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003. [http://cbcl.mit.edu/projects/cbcl/publications/ps/max-vis-cortex-03.pdf link]<br />
<br />
* Rolls,E.T. (1997) A neurophysiological and computational approach to the functions of the temporal lobe cortical visual areas in invariant object recognition. Chapter 9, pp. 184-220 in Computational and Psychophysical Mechanisms of Visual Coding, eds. M.Jenkin and L.Harris. Cambridge University Press: Cambridge. [http://www.cns.ox.ac.uk/publications.html link]<br />
<br />
====Sleep====<br />
* <big>&#10004;</big> Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114. [http://redwood.berkeley.edu/~cadieu/pdf/CrickMitchisonDreamSleep1983.pdf link]<br />
<br />
* <big>&#10004;</big> Sutherland GR, McNaughton B., Memory trace reactivation in hippocampal and neocortical neuronal ensembles., Curr Opin Neurobiol. 2000 Apr;10(2):180-6 [http://redwood.berkeley.edu/~cadieu/pdf/SutherlandMcNaughtonMemoryReactivation1999.pdf link]<br />
<br />
==== Theories of Hippocampus ====<br />
<br />
* Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738. [http://www.science.mcmaster.ca/Psychology/sb.html link]<br />
<br />
* Leutgeb, S., Leutgeb, J.K., Moser, M.-B., and Moser, E.I. (2005). Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15, 738-746. [http://www.cbm.ntnu.no/publications link]<br />
<br />
* [http://www.bris.ac.uk/synaptic/research/projects/memory/spatialmem.htm place cells]<br />
<br />
* Hasselmo, M.E. and McClelland, J.L. (1999) Neural models of memory. Curr. Opinion Neurobiol. 9: 184-188. [http://people.bu.edu/hasselmo/HasselmoMcClelland.pdf link]<br />
<br />
==== Motor System ====<br />
<br />
* Körding, KP. and Wolpert, D. (2004) Bayesian Integration in Sensorimotor Learning, Nature 427:244-247. [http://www.koerding.com/pubs/koerdingNature2004.pdf link]<br />
<br />
* <big>&#10004;</big> Georgopoulos AP. Neuron. 1994 Aug;13(2):257-68. New concepts in generation of movement. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8060612&dopt=Abstract link]<br />
<br />
* <big>&#10004;</big> Todorov E Direct cortical control of muscle activation in voluntary arm movements: a model. (2000) Nature Neuroscience 3(4): 391-398 [http://cogsci.ucsd.edu/~todorov/papers/mi.pdf link]<br />
<br />
* Todorov E. On the role of primary motor cortex in arm movement control (2003) In Progress in Motor Control III, ch 6, pp 125-166, Latash and Levin (eds), Human Kinetics [http://cogsci.ucsd.edu/~todorov/papers/MotorCortex.pdf link]<br />
<br />
* Scott, S.H. (2004) Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5:532-546. [http://limb.biomed.queensu.ca/publications/optimal_feedback_control_and_the_neural_basis.pdf link]<br />
<br />
==== Dynamics ====<br />
<br />
See [[Dynamics Reading Club]] site</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8502TCN2016-03-31T00:08:31Z<p>Dpaiton: /* Topics in Computational Neuroscience */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
[Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=Past_TCN_Papers&diff=8501Past TCN Papers2016-03-31T00:07:27Z<p>Dpaiton: /* Summer 2010 */</p>
<hr />
<div>=== Syllabus (Topics and Readings) ===<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== Summer 2010 ===<br />
<br />
* [June 10] <br />
** Jarzynski, Nonequilibrium work relations: foundations and applications. [http://tinyurl.com/2fxyowa]<br />
** Crooks, Nonequilibrium Measurements of Free Energy Differences for Microscopically Reversible Markovian Systems. [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.63.4984&rep=rep1&type=pdf]<br />
** Crooks, Beyond Boltzmann-Gibbs statistics: Maximum entropy hyperensembles out of equilibrium [http://www.google.com/url?sa=t&source=web&cd=1&ved=0CBIQFjAA&url=http%3A%2F%2Fthreeplusone.com%2Fpubs%2Ffulltext%2FCrooks2007b.pdf&ei=G4QZTJKNKoLSNYCj8PcI&usg=AFQjCNEPftRKxOfq3hMWSCxpOQeTyZErZA&sig2=2xhgXNLHQQuWcN82MJBU_g]<br />
<br />
* [May 20] Truccolo et al., Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes [http://www.nature.com/neuro/journal/v13/n1/abs/nn.2455.html/]<br />
<br />
=== Spring 2010 ===<br />
<br />
* [May 13] A review of active learning (no paper).<br />
<br />
* [Apr 29] Ranzato and Hinton, Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines [http://www.cs.toronto.edu/~ranzato/publications/ranzato_cvpr2010.pdf]<br />
<br />
* [Apr 22] Boureau et al., Learning Mid-Level Features For Recognition [http://yann.lecun.com/exdb/publis/pdf/boureau-cvpr-10.pdf]<br />
<br />
* [Apr 15] Neghaban et al., A unified framework for the analysis of regularized $M$-estimators.[http://www.eecs.berkeley.edu/~sahand_n/highdimrates.pdf]<br />
<br />
* [Apr 6] Lucke et al., Occlusive components analysis. [http://www.fias.uni-frankfurt.de/~luecke/Publications/LuckeEtAl2009.pdf]<br />
<br />
* [Mar 25] Bießmann et al., Decision-related activity in sensory neurons reflects more than a neuron's causal effect.[http://www.springerlink.com/content/e1425487365v2227]<br />
<br />
* [Mar 18] Itskov and Abbott, Pattern Capacity of a Perceptron for Sparse Discrimination. [http://neurotheory.columbia.edu/~larry/ItskovPRL08.pdf]<br />
<br />
* [Mar 11] Nienborg and Cumming, Decision-related activity in sensory neurons reflects more than a neuron's causal effect.[http://www.nature.com/nature/journal/v459/n7243/full/nature07821.html]<br />
<br />
* [Feb 18] Masse et al., Olfactory Information Processing in Drosophila. [http://download.cell.com/current-biology/pdf/PIIS0960982209013013.pdf]<br />
<br />
* [Feb 11] Yaghoobi et al., Dictionary Learning for Sparse Approximations with the Majorization Method. [http://www.see.ed.ac.uk/%7Es0574225/YBD_DLMM_TSP.pdf]<br />
<br />
* [Feb 4] Ecker et al., Decorrelated Neuronal Firing in Cortical Microcircuits. [http://www.sciencemag.org/cgi/content/abstract/327/5965/584]<br />
* [Feb 4] Renart et al., The Asynchronous State in Cortical Circuits. [http://www.sciencemag.org/cgi/content/abstract/327/5965/587]<br />
<br />
* [Jan 28] Recht et al., Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization. [http://pages.cs.wisc.edu/~brecht/papers/07.rfp.lowrank.pdf]<br />
<br />
=== Fall 2009 ===<br />
<br />
* [Dec 3] Kolgin et al., Frequency of gamma oscillations routes flow of information in the hippocampus. [http://www.nature.com/nature/journal/v462/n7271/abs/nature08573.html]<br />
<br />
* [Nov 17] Graham, Chandler, Field. Can the theory of ‘‘whitening’’ explain the center-surround properties of retinal ganglion cell receptive fields? [http://math.dartmouth.edu/~dgraham/dg/rgc-paper.pdf]<br />
<br />
* [Nov 12] Lighthill and AI community debate, 1973. [http://www.aiai.ed.ac.uk/events/lighthill1973/]<br />
<br />
* [Oct 29, Nov 5] George, Hawkins, Towards a Mathematical Theory of Cortical Micro-circuits. [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000532]<br />
<br />
* [Oct 22]: Berkes et al., A Structured Model of Video Reproduces Primary Visual Cortical Organisation.[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000495]<br />
<br />
* [Oct 15]: Petreanu et al., The subcellular organization of neocortical excitatory connections. [http://www.nature.com/nature/journal/v457/n7233/pdf/nature07709.pdf]<br />
<br />
* [Oct 15]: Arenz et al. The contribution of single synapses to sensory representation in vivo. [http://www.sciencemag.org/cgi/content/full/321/5891/977]<br />
<br />
* [Oct 1,8]: Kobilarov et al., Lie Group Integrators for Animation and Control of Vehicles.[http://www.geometry.caltech.edu/pubs/KCD09.pdf]<br />
<br />
==== Last papers covered ====<br />
<br />
<big>&#10004;</big> indicates papers we've read. The others are suggestions for future readings.<br />
<br />
* <big>&#10004;</big> Tutorial on Conjugate Gradient - no paper but ''An Introduction to the Conjugate Gradient Method Without the Agonizing Pain'' by Jonathan Richard Shewchuk might be useful: [http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf link] - Presenter: <font color="#ff0000">Chris Hillar</font><br />
<br />
* <big>&#10004;</big> Tutorial on Natural Gradient - no paper but ''Amari. Natural gradient works efficiently in learning. Neural Computation'', Amari 1998 might be useful: [http://www.mitpressjournals.org/doi/abs/10.1162/089976698300017746 link] - Presenter: <font color="#ff0000">Jascha</font><br />
<br />
* <big>&#10004;</big> ''Distilling Free-Form Natural Laws from Experimental Data'', Michael Schmidt1 and Hod Lipson 2009. Science 324(59233): [http://sciencemag.org/cgi/content/abstract/324/5923/81 link] - Presenter: <font color="#ff0000">Daniel</font><br />
<br />
* <big>&#10004;</big> ''Learning Optical Flow'', Deqing Sun, Stefan Roth, J. P. Lewis, and Michael Black. EECV 2008: [http://www.gris.informatik.tu-darmstadt.de/~sroth/pubs/eccv2008.pdf link] - Presenter: <font color="#ff0000">Jack</font><br />
<br />
* <big>&#10004;</big> ''Decision-theoretic saliency: computational principles, biological plausibility, and implications for neurophysiology and psychophysics'', Gao D, Vasconcelos N. Neural Comput. 2009 Jan;21(1):239-71.: [http://www.ncbi.nlm.nih.gov/pubmed/19210172?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum link] - Presenter: <font color="#ff0000">Daniel</font><br />
<br />
* <big>&#10004;</big> ''Temporal interactions between cortical rhythms'', Roopun AK, Kramer MA, Carracedo LM, Kaiser M, Davies CH, Traub RD, Kopell NJ and Whittington MA. (2008) Front. Neurosci. 2,2:145-154. doi:10.3389/neuro.01.034.2008: [http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2622758 link] - Presenter: <font color="#ff0000">Kilian</font><br />
<br />
* <big>&#10004;</big> ''Recognition of natural scenes from global properties: seeing the forest without representing the trees'', Greene, M.R. & Oliva, A. 2009 Cognitive Psychology Vol. 58(2), pp. 137-76 [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WCR-4TB9P8G-1&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=3033c96c7f8db6b8a6f6761be811ad79 link] - Presenter: <font color="#ff0000">Paul Ivanov</font><br />
<br />
* <big>&#10004;</big> ''Cogito componentiter ergo sum'', Lars Kai Hansen and Ling Feng. Lecture Notes in Computer Science, 2006: [http://www.springerlink.com/index/j456687g40246n57.pdf link] - Presenter: <font color="#ff0000">?</font><br />
<br />
* <big>&#10004;</big> Tutorial on representation theory and invariance - no paper but ''A novel set of rotationally and translationally invariant features for images based on the non-commutative bispectrum'' by Risi Kondor might be useful: [http://arxiv.org/abs/cs.CV/0701127 link] - Presenter: <font color="#ff0000">Chris Hillar</font><br />
<br />
* <big>&#10004;</big> ''If I Were You: Perceptual Illusion of Body Swapping'', Petkova VI, Ehrsson HH 2008. PLoS ONE 3(12): e3832 doi:10.1371/journal.pone.0003832 [http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0003832 link] - Presenter: <font color="#ff0000">Jascha</font><br />
<br />
* <big>&#10004;</big> ''Multisensory integration in macaque visual cortex depends on cue reliability'', Morgan ML, Deangelis GC, Angelaki DE., Neuron. 2008 Aug 28;59(4):662-73. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSS-4T9MB77-H&_user=4420&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000059607&_version=1&_urlVersion=0&_userid=4420&md5=af04fa45c08906f07d4f7d270335cdc6 link] - Presenter: <font color="#ff0000">Rama Natarajan</font><br />
<br />
* <big>&#10004;</big> ''Design of Linear Equalizers Optimized for the Structural Similarity Index'', S. Channappayya, Alan Bovik, C. Caramanis, R. Heath Jr; IEEE Transactions on Image Processing, Vol 17 NO 6 June 2008 [http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=4483677&isnumber=4515964&punumber=83&k2dockey=4483677@ieeejrns&query=%28%28bovik%29%3Cin%3Emetadata%29&pos=4&access=no link] - Presenter: <font color="#ff0000">Jimmy</font><br />
<br />
* <big>&#10004;</big> ''Kalman filtered compressed sensing'', Namrata Vaswani, ICIP 2008 [http://arxiv.org/abs/0804.0819 link] - Presenter: <font color="#ff0000">Jack</font><br />
<br />
* <big>&#10004;</big> ''Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades'', Shlomit Yuval-Greenberg, Orr Tomer, Alon S. Keren, Israel Nelken and Leon Y. Deouell, Neuron, 58, pp. 429–441, 2008. [http://www.neuron.org/content/article/abstract?uid=PIIS0896627308003012 link] - Presenter: <font color="#ff0000">Paul and Tim</font><br />
<br />
* <big>&#10004;</big> ''Internally Generated Reactivation of Single Neurons in Human Hippocampus During Free Recall'', Hagar Gelbard-Sagiv, Roy Mukamel, Michal Harel, Rafael Malach, Itzhak Fried, Science, Published Online September 4, 2008. [http://www.sciencemag.org/cgi/content/short/1164685 link] - Presenter: <font color="#ff0000">Will</font><br />
<br />
* <big>&#10004;</big> ''Saliency Detection: A Spectral Residual Approach'', Xiaodi Hou and Liqing Zhang, in Proc. of CVPR '07, pp. 1-8, 2007. [http://bcmi.sjtu.edu.cn/~houxiaodi/papers/CVPR07.pdf link] - Presenter: <font color="#ff0000">Chetan</font><br />
<br />
* <big>&#10004;</big> ''The information bottleneck method'', N. Tishby, F.C. Pereira and W. Bialek, in Proceedings of the 37th Annual Allerton Conference on Communication, Control and Computing, B. Hajek and R.S. Sreenivas eds, pp. 368-377 (University of Illinois, 1999). [http://www.princeton.edu/~wbialek/our_papers/tishby+al_99.pdf link] - Presenter: <font color="#ff0000">Pierre</font><br />
<br />
* <big>&#10004;</big> ''Evolutionary expansion and anatomical specialization of synapse proteome complexity'', R. D. Emes et al., Nature Neuroscience, vol. 11, no. 7, pp. 799-806, June 2008. [http://www.nature.com/neuro/journal/v11/n7/full/nn.2135.html link] - Presenter: <font color="#ff0000">Jeff</font><br />
<br />
* <big>&#10004;</big> ''Stability of the fittest: organizing learning through retroaxonal signals'', Ken Harris, Trends Neurosci, 31(3), pp. 130-136, 2008. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T0V-4RS3TBR-1&_user=4420&_coverDate=03%2F31%2F2008&_alid=770804385&_rdoc=1&_fmt=high&_orig=search&_cdi=4872&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000059607&_version=1&_urlVersion=0&_userid=4420&md5=38891fcb62351e3bfa79f00214a3dbfb link] - Presenter: <font color="#ff0000">Will</font><br />
<br />
* <big>&#10004;</big> ''Object recognition with cortex-like mechanisms'', T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber and T. Poggio, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (3), pp. 411-426, 2007. [http://web.mit.edu/serre/www/publications/Serre_etal_PAMI07.pdf link]- Presenter: <font color="#ff0000">Jack</font><br />
<br />
* <big>&#10004;</big> ''Nonlinear extraction of 'Independent Components' of elliptically symmetric densities using radial Gaussianization'', S. Lyu and E. P. Simoncelli, Computer Science Technical Report TR2008-911, Apr 2008. [http://www.cns.nyu.edu/pub/lcv/lyu08a.pdf link] - Presenter: <font color="#ff0000">Bruno</font><br />
<br />
* <big>&#10004;</big> ''Relating neural dynamics to neural coding'', G. Bard Ermentrout, Roberto F. Galàn and Nathaniel N. Urban, Physical Review Letters, vol. 99, no. 24, December 2007. [http://www.andrew.cmu.edu/user/rfgalan/pdf/Ermentrout_etal_PRL07.pdf link] - Presenter: <font color="#ff0000">Ariel</font><br />
<br />
* <big>&#10004;</big> ''Spatio-temporal correlations and visual signaling in a complete neuronal population'', Jonathan W. Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, Eero P. Simoncelli, Nature (to appear), 2008. - Presenter: <font color="#ff0000">Jonathon Shlens</font><br />
<br />
* <big>&#10004;</big> ''Learning Compressed Sensing'', Yair Weiss and Hyun Sung Chang and William T. Freeman, Allerton 2007. [http://www.cs.huji.ac.il/~yweiss/allerton-final.pdf link] - Presenter: <font color="#ff0000">Pierre</font><br />
<br />
* <big>&#10004;</big> ''Sparse and shift-invariant feature extraction from non-negative data'', P. Smaragdis, B. Raj, and M.V. Shashanka, In proceedings IEEE International Conference on Audio and Speech Signal Processing, April 2008. [http://web.media.mit.edu/~paris/Paris_Smaragdis/Paris_Smaragdis_web_page_files/smaragdis-icassp2008.pdf link] - Presenter: <font color="#ff0000">Vivienne</font><br />
<br />
* <big>&#10004;</big> ''Context and hierarchy in a probabilistic image model'', Y. Jin and S. Geman, CVPR (2) 2006, pp. 2145-2152. [http://www.dam.brown.edu/people/geman/Homepage/Compositionality%20in%20computer%20vision/cvpr06_final.pdf link] - Presenter: <font color="#ff0000">Amir</font><br />
<br />
* <big>&#10004;</big> ''Entrainment of neuronal oscillations as a mechanism of attentional selection'', Peter Lakatos, George Karmos, Ashesh D. Mehta, Istvan Ulbert, Charles E. Schroeder, Science, vol. 320, no. 5872, pp. 110-113, April 2008. [http://www.sciencemag.org/cgi/content/full/320/5872/110 link] - Presenter: <font color="#ff0000">Gianluca</font><br />
<br />
* <big>&#10004;</big> ''The missing memristor found'', Dmitri B. Strukov, Gregory S. Snider, Duncan R. Stewart, R. Stanley Williams, Nature, vol. 453, pp. 80-83, May 2008. - Presenter: <font color="#ff0000">Chris</font><br />
<br />
* <big>&#10004;</big> ''80 million tiny images: a large dataset for non-parametric object and scene recognition'', A. Torralba, R. Fergus, W. T. Freeman, submitted to IEEE PAMI, October 2007. [http://people.csail.mit.edu/torralba/tmp/tiny.pdf link] - Presenter: <font color="#ff0000">Jascha</font><br />
<br />
* <big>&#10004;</big> ''Decoupling through Synchrony in Neuronal Circuits with Propagation Delays'', E. V. Lubenov and A. G. Siapas. Neuron, vol. 58, 2008, pp. 118-131. [http://www.neuron.org/content/article/abstract?uid=PIIS0896627308001281 link] - Presenter: <font color="#ff0000">Amir</font><br />
<br />
* <big>&#10004;</big> ''The role of learning in 3-D form perception'', P. Sinha and T. Poggio. Nature, vol. 384, no 6608, 1996, pp. 460-463. [http://web.mit.edu/bcs/sinha/papers/sinha_poggio_1996.pdf link] - Presenter: <font color="#ff0000">Charles </font><br />
<br />
* <big>&#10004;</big> ''Sparse Feature Learning for Deep Belief Networks'', M. Ranzato, Y. Boureau, Y. LeCun. Advances in Neural Information Processing Systems 20 (NIPS 2007). [http://www.cs.nyu.edu/~ranzato/publications/ranzato-nips07.pdf link] - Presenter: <font color="#ff0000">Jimmy</font><br />
<br />
* <big>&#10004;</big> ''Phase-of-Firing Coding of Natural Visual Stimuli in Primary Visual Cortex'', Marcelo A. Montemurro, Malte J. Rasch, Yusuke Murayama, Nikos K. Logothetis, and Stefano Panzeri. Current Biology, vol. 18, no. 5, Mar 2008, pp. 375-380. [http://www.current-biology.com/content/article/abstract?uid=PIIS0960982208001681 link] - Presenter: <font color="#ff0000"> Kilian </font><br />
<br />
* <big>&#10004;</big> ''Multisensory auditory–visual interactions during early sensory processing in humans: a high-density electrical mapping study'', Sophie Molholm, Walter Ritter, Micah M. Murray, Daniel C.Javitt, Charles E. Schroeder and John J. Foxe. Cognitive Brain Research, vol. 14, no. 1, Jun 2002, pp. 115-128. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6SYV-453NT51-3&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=2411980aadd64b5e82e152d090c8cdbd link] - Presenter: <font color="#ff0000"> Gianluca </font><br />
<br />
* <big>&#10004;</big> ''The sleep switch: hypothalamic control of sleep and wakefulness'', Clifford B. Saper, Thomas C. Chou and Thomas E. Scammell, Trends in Neurosciences, Vol. 24, No. 12, Dec 2001. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T0V-44FBS07-11&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=54f2cf21b3a8f2dd7effb8732a331506 link] - Presenter: <font color="#ff0000"> Will </font><br />
<br />
* <big>&#10004;</big> ''Sparse time-frequency representations'', T. J. Gardner and M. O. Magnasco, PNAS, vol. 103, no. 16, pp. 6094-6099, 2006. [http://www.pnas.org/cgi/content/abstract/103/16/6094 link] - Presenter: <font color="#ff0000"> Marcelo Magnasco</font><br />
<br />
* <big>&#10004;</big> ''Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication'', H. Jaeger and H. Haas, Science, Vol. 304, Issue 5667, pp. 78-80, 2004. [http://www.sciencemag.org/cgi/content/abstract/304/5667/78 link] - Presenter: <font color="#ff0000">Jascha</font><br />
<br />
* <big>&#10004;</big> ''Does spatial invariance result from insensitivity to change?'', F. A. A. Kingdom, D. J. Field and A. Olmos, Journal of Vision, 7(14):11, 1-13, Dec. 2007. [http://journalofvision.org/7/14/11/ link] - Presenter: <font color="#ff0000"> Bruno Olshausen </font><br />
<br />
* <big>&#10004;</big> ''How Behavioral Constraints May Determine Optimal Sensory Representations'', Emilio Salinas, PLoS Biology, 6:12, Dec. 2006. [http://biology.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pbio.0040387 link] - Presenter: <font color="#ff0000"> Lavi Secundo </font><br />
<br />
* <big>&#10004;</big> Convexity, disparity, and depth perception: internalization of natural scene statistics. Johannes Burge, Charless C. Fowlkes, Martin S. Banks. DRAFT. Presenter: <font color="#ff0000"> Johannes Burge </font><br />
<br />
* <big>&#10004;</big> The K-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation. IEEE Trans. On Signal Processing, Vol. 54, no. 11, pp. 4311-4322, November 2006. [http://www.cs.technion.ac.il/~elad/publications/journals/2004/32_KSVD_IEEE_TSP.pdf link] Presenter: <font color="#ff0000"> Gianluca Monaci </font><br />
<br />
* <big>&#10004;</big> Image Denoising with Nonparametric Hidden Markov Trees. J. Kivinen, E. Sudderth, and M. Jordan. ICIP 2007. [http://ssg.mit.edu/~esuddert/papers/icip07.pdf link] Presenter: <font color="#ff0000"> Jack Culpepper </font><br />
<br />
* <big>&#10004;</big> Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex. Mukamel R, Gelbard H, Arieli A, Hasson U, Fried I, Malach R. Science. 2005 Aug 5;309(5736):951-4. [http://www.sciencemag.org/cgi/content/abstract/309/5736/951?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&fulltext=Coupling+between+neuronal+firing%2C+field+potentials%2C+and+FMRI+in+human+auditory+cortex.&searchid=1&FIRSTINDEX=0&resourcetype=HWCIT link] Presenter: <font color="#ff0000"> Tim Blanche </font><br />
<br />
* <big>&#10004;</big> The design and use of steerable filters. W. T. Freeman and E. H. Adelson, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 13, no. 9, pp. 891 - 906, September, 1991. [http://people.csail.mit.edu/billf/papers/steerpaper91FreemanAdelson.pdf link]Presenter: <font color="#ff0000"> Jimmy Wang </font><br />
<br />
* <big>&#10004;</big> Problem reduction, renormalization, and memory. Commun. Appl. Math. Comput. Sci. 1 1-27. Chorin, Alexandre J. and Stinis, Panagiotis (2006). Presenter: <font color="#ff0000"> Amir and Fritz </font><br />
<br />
* <big>&#10004;</big> Learning Lie Groups of Visual Invariance. Xu Miao and Rajesh P.N. Rao. Neural Computation 19,2665-2693 (2007). Presenter: <font color="#ff0000"> Pierre Garrigues </font><br />
<br />
* <big>&#10004;</big> Correlation between neural spike trains increases with firing rate. Jaime de la Rocha, Brent Doiron, Eric Shea-Brown, Kresimir Josic & Alex Reyes. Nature 448, 802-806 (16 August 2007) [http://www.nature.com/nature/journal/v448/n7155/abs/nature06028.html link]. Presenter: <font color="#ff0000"> Chris Rozell </font><br />
<br />
* <big>&#10004;</big> Adaptation to Stimulus Contrast and Correlations during Natural Visual Stimulation. Nicholas A Lesica, Jianzhong Jin, Chong Weng, Chun-I Yeh, Daniel A Butts, Garrett B Stanley, Jose-Manuel Alonso. Neuron 2007 vol. 55 pp. 479-91. Presenter: <font color="#ff0000"> Tim Blanche </font><br />
<br />
* <big>&#10004;</big> Quest for a Stochastic Grammar of Images. Song-Chun Zhu and David Mumford. To appear in Foundations and Trends in Computer Graphics and Vision, 2007. [http://www.stat.ucla.edu/~sczhu/papers/Grammar_quest.pdf link]. Presenter: <font color="#ff0000"> Amir </font><br />
<br />
* <big>&#10004;</big> Slow feature analysis yields a rich repertoire of complex cell properties. Berkes, P. and Wiskott, L. (2005). Journal of Vision, 5(6), 579-602. [http://journalofvision.org/5/6/9/article.aspx link]. Presenter: <font color="#ff0000"> Charles Cadieu </font><br />
<br />
* <big>&#10004;</big> Unsupervised learning of image transformations. Memisevic, R. and Hinton, G. E. Computer Vision and Pattern Recognition 2007 [http://www.cs.toronto.edu/~rfm/pubs/cvpr07.pdf link]. Presenter: <font color="#ff0000"> Jack Culpepper </font><br />
<br />
* <big>&#10004;</big> The thalamus as a monitor of motor outputs. R. W. Guillery and S. M. Sherman. Phil. Trans. R. Soc. Lond. B (2002) 357, 1809-1821. Presenter: <font color="#ff0000"> Jascha Sohl-Dickstein </font><br />
<br />
* <big>&#10004;</big> Recovering Intrinsic Images from a Single Image. Tappen, M. F., Freeman, W. T., and Adelson, E. H.. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9): 1459 - 1472, (2005). [http://web.mit.edu/persci/people/adelson/documents/Tappen_PAMI_27-9.pdf link] Presenter: <font color="#ff0000"> Chetan Nandakumar </font><br />
<br />
* <big>&#10004;</big> N. Ay, D. Polani. Information Flows in Causal Networks. Advances in Complex Systems, submitted. Santa Fe Institute Working Paper 06-05-014. [http://www.santafe.edu/research/publications/workingpapers/06-05-014.pdf link] Presenter: <font color="#ff0000"> Joe Makin </font><br />
<br />
* <big>&#10004;</big> Der R., Hesse F., Liebscher R.: Contingent robot behavior from self-referential dynamical systems. Autonomous robots submitted. [http://www.informatik.uni-leipzig.de/~der/Veroeff/autorob.pdf link] Presenter: <font color="#ff0000"> Fritz Sommer </font><br />
<br />
* <big>&#10004;</big> Z Wang, A C Bovik and E P Simoncelli. Structural approaches to image quality assessment. Chapter 8.3 in Handbook of Image and Video Processing, 2nd Edition, ed. Alan Bovik, Academic Press, 2005. [http://www.cns.nyu.edu/pub/lcv/wang05c-preprint.pdf link] Presenter: <font color="#ff0000"> Jimmy Wang </font><br />
<br />
* <big>&#10004;</big> O. Ben-Shahar and S.W. Zucker. Geometrical Computations Explain Projection Patterns of Long Range Horizontal Connections in Visual Cortex, in Neural Computation, 16(3): 445-476, 2004. [http://www.cs.yale.edu/~shahar/Publications/NC2004.pdf link] Presenter: <font color="#ff0000"> Pierre Garrigues </font><br />
<br />
* <big>&#10004;</big> Zhaoping L. (2005) Border Ownership from Intracortical Interaction in Visual Area V2 , in NEURON, Vol. 47, 143-153 [http://www.gatsby.ucl.ac.uk/~zhaoping/prints/ZhaopingNEURON2005JournalVersion.pdf link] Presenter: <font color="#ff0000"> Paul King </font><br />
<br />
* <big>&#10004;</big> Nakayama, K. , He, Z.J. and Shimojo, S. Visual surface representation: a critical link between lower-level and higher level vision. In Kosslyn, S.M. and Osherson, D.N. Vision. In Invitation to Cognitive Science. M.I.T. Press, p. 1-70, 1995 [http://visionlab.harvard.edu/Members/Ken/Ken%20papers%20for%20web%20page/077NKHeShimojoMIT1995b.pdf link] Presenter: <font color="#ff0000"> Jascha Sohl-Dickstein </font><br />
<br />
* <big>&#10004;</big> Sinha, P., Balas, B.J., Ostrovsky, Y., & Russell, R."Face Recognition by Humans: 19 results all computer vision researchers should know about." Proceedings of the IEEE. Vol. 94, No. 11, 1948-1962 (2006) [web.mit.edu/bcs/sinha/papers/20Results_2005.pdf link] Presenter: <font color="#ff0000"> Chetan Nandakumar </font><br />
<br />
* <big>&#10004;</big> Chandler DM, and Field DJ. "Estimates of the Information Content and Dimensionality of Natural Scenes from Proximity Distributions." Journal of the Optical Society of America, 2006 (in press) [http://redwood.psych.cornell.edu/manuscripts/chandler_field_xentropy_2006.pdf link] Presenter: <font color="#ff0000"> Paul Ivanov </font><br />
<br />
* <big>&#10004;</big> Z. Tu, X. Chen, A.L. Yuille, and S.C. Zhu. "Image Parsing: Unifying Segmentation, Detection, and Recognition". Proceedings of ICCV 2003. [http://www.stat.ucla.edu/~yuille/pubs/impaired/iccv_parsing.pdf link] Presenter: <font color="#ff0000"> Jimmy Wang </font><br />
<br />
* <big>&#10004;</big> E. Schneidman, M. Berry, R. Segev and W. Bialek. "Weak pairwise correlations imply strongly correlated network states in a neural population". Nature 440, 1007-1012 (20 April 2006) [http://www.nature.com/nature/journal/v440/n7087/full/nature04701.html link] Presenter: <font color="#ff0000"> Pierre Garrigues </font><br />
<br />
* <big>&#10004;</big> S. Ullman, S. Soloviev. "Computation of Pattern Invariance in Brain-like Structures". Neural Networks 1999. [http://www.wisdom.weizmann.ac.il/~shimon/papers/NN12_99.pdf link] Presenter: <font color="#ff0000"> Charles Cadieu </font><br />
<br />
* <big>&#10004;</big> P. Bak, C. Tang, and K. Wiesenfeld. "Self-Organized Critically: An Explanation of 1/f Noise". Physical Review Letters, July 1987. Presenter: <font color="#ff0000"> Kilian Koepsell </font><br />
<br />
* <big>&#10004;</big> A. Hyvarinen. "Estimation of Non-Normalized Statistical Models by Score Matching". Journal of Machine Learning Research 6 (2005) 695-709. Presenter: <font color="#ff0000"> Jascha Sohl-Dickstein </font><br />
<br />
* <big>&#10004;</big> M. Ranzato, C.S. Poultney, S. Chopra and Y. LeCun. "Efficient Learning of Sparse Overcomplete Representations with an Energy-Based Model". Advances in Neural Information Processing Systems 19, in Scholkopf et al. (eds), MIT Press, Cambridge, MA, 2007. Presenter: <font color="#ff0000"> Pierre Garrigues </font><br />
<br />
* <big>&#10004;</big> Liam Paninski, Jonathan Pillow and Eero Simoncelli. Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. Neural Computation, 2004. Presenter: <font color="#ff0000"> Amir </font><br />
<br />
* <big>&#10004;</big> Randall C. O'Reilly. Biologically Based Computational Models of High-Level Cognition. Science Oct. 2006. Presenter: <font color="#ff0000"> Charles Cadieu </font><br />
<br />
* <big>&#10004;</big> Todorov, E. and Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5, 1226-1235, 2002.<br />
<br />
* <big>&#10004;</big> Asari, H., Pearlmutter, B. A. and Zador, A. M. Sparse Representations for the Cocktail Party Problem. The Journal of Neuroscience, July 12, 2006.<br />
<br />
* <big>&#10004;</big> Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.<br />
<br />
* <big>&#10004;</big> P. Dayan, Image, Frames, and Connectionist Hierarchies. Neural Computation 18, 2293-2319 (2006)<br />
<br />
* <big>&#10004;</big> T. Blaschke, P. Berkes and L. Wiskott, What Is the Relation Between Slow Feature Analysis and Independent Component Analysis? Neural Computation 18, 2495-2508 (2006)<br />
<br />
* <big>&#10004;</big> T.J. Gardner, and M.O. Magnasco, Sparse time-frequency representations PNAS 103: 6094-6099, April 18, 2006 [http://www.pnas.org/cgi/reprint/103/16/6094.pdf paper]<br />
<br />
* <big>&#10004;</big> B.L. McNaughton, F.P. Battaglia, O. Jensen, E.I. Moser and M.B. Moser, Path integration and the neural basis of the 'cognitive map'. Nature Reviews Neuroscience 7, 663-678 (August 2006) [http://www.nature.com/nrn/journal/v7/n8/full/nrn1932.html paper]<br />
<br />
* <big>&#10004;</big> Karklin, Lewicki. A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals. Neural Computation. 2005;17:397-423. [http://www.mitpressjournals.org/doi/abs/10.1162/0899766053011474 paper] [http://video.google.com/videoplay?docid=-5115453018099427284&q=msri MSRI talk]<br />
<br />
* <big>&#10004;</big> Perspectives: Jose-Manuel Alonso, Neurons Find Strength Through Synchrony in the Brain, Science, 16 June 2006, Vol. 312. no. 5780, pp. 1604 - 1605 [http://www.sciencemag.org/cgi/content/short/312/5780/1604 link]<br />
<br />
* <big>&#10004;</big> Randy M. Bruno and Bert Sakmann, Cortex Is Driven by Weak but Synchronously Active Thalamocortical Synapses, Science, 16 June 2006, Vol. 312. no. 5780, pp. 1622 - 1627 [http://www.sciencemag.org/cgi/content/short/312/5780/1622 link]<br />
<br />
* <big>&#10004;</big> David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature 440, 680-683 (30 March 2006) [http://www.nature.com/nature/journal/v440/n7084/pdf/nature04587.pdf link]<br />
<br />
* <big>&#10004;</big> Guetig, Sompolinsky. The Tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience 2006; 9:420-428. [http://www.nature.com/neuro/journal/v9/n3/pdf/nn1643.pdf link]<br />
<br />
* <big>&#10004;</big> E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]<br />
<br />
: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]<br />
<br />
* <big>&#10004;</big> D. Y. Tsao, W. A. Freiwald, R. B. H. Tootell, M. S. Livingstone, A Cortical Region Consisting Entirely of Face-Selective Cells, Science 3 February 2006; Vol. 311. no. 5761, pp. 670 - 674. [http://www.sciencemag.org/cgi/content/full/311/5761/670 link]<br />
<br />
: Perspectives: Kanwisher, "What's in a Face?". [http://www.sciencemag.org/cgi/content/full/311/5761/617 link]<br />
<br />
* <big>&#10004;</big> Harris Nover, Charles H. Anderson, and Gregory C. DeAngelis. A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance. J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/content/full/25/43/10049?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&author1=anderson&searchid=1&FIRSTINDEX=0&resourcetype=HWCIT link]<br />
<br />
* <big>&#10004;</big> Randy M. Bruno and Bert Sakmann, Cortex Is Driven by Weak but Synchronously Active Thalamocortical Synapses, Science, 16 June 2006, Vol. 312. no. 5780, pp. 1622 - 1627 [http://www.sciencemag.org/cgi/content/short/312/5780/1622 link]<br />
<br />
: Perspectives: Jose-Manuel Alonso, Neurons Find Strength Through Synchrony in the Brain, Science, 16 June 2006, Vol. 312. no. 5780, pp. 1604 - 1605 [http://www.sciencemag.org/cgi/content/short/312/5780/1604 link]<br />
<br />
* <big>&#10004;</big> C Whitney & P Cornelissen (2005). Letter-position encoding and dyslexia. Journal of Research in Reading 28, 274-301. [http://www.cs.umd.edu/~shankar/cwhitney/Papers/JRR.pdf paper]<br />
<br />
==== Early Work ====<br />
<br />
* <big>&#10004;</big> K. A. C. Martin, The Pope and grandmother−a frog's-eye view of theory, Nature Neuroscience 3, 1169 (2000) [http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1169.html link]<br />
<br />
* <big>&#10004;</big> Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" [http://jerome.lettvin.info/WhatTheFrogsEyeTellsTheFrogsBrain.pdf link]<br />
<br />
* <big>&#10004;</big> Barlow HB (1972) Single Neurons and Sensation: A neuron doctrine for perceptual psychology. Perception 1, 371-394. [http://redwood.berkeley.edu/~amir/pdf/Barlow1972.pdf link]<br />
<br />
* Marvin Minsky, Steps Toward Artificial Intelligence [http://portal.acm.org/citation.cfm?id=216408.216442&coll=GUIDE&dl=GUIDE link]<br />
<br />
* McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” (1943) [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104377 link]<br />
<br />
* Marr, selection from Vision; Artificial Intelligence—a personal view, by David Marr [http://scholar.google.com/url?sa=U&q=https://dspace.mit.edu/handle/1721.1/5776%3Fmode%3Dsimple link]<br />
<br />
* Readings from Dartmouth Conf. 1956 proceedings<br />
* Rosenblatt, Frank (1962). Principles of neurodynamics. New York: Spartan. Cf. Rumelhart, D.E., J. L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing vol. 1&2. Cambridge: MIT. [http://scholar.google.com/scholar?hl=en&lr=&q=rosenblatt+principles+of+neurodynamics&btnG=Search link]<br />
<br />
* "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint" (with H. Dreyfus),Daedulus, Winter 1988 [http://portal.acm.org/citation.cfm?id=63323.66521 link]<br />
<br />
==== Coding ====<br />
<br />
* <big>&#10004;</big> Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004. [http://dx.doi.org/10.1016/j.plrev.2004.06.001 link]<br />
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* <big>&#10004;</big> Pouget, A, Dayan, P & Zemel, RS (2000). Information processing with population codes. Nature Reviews Neuroscience, 1 , 125-132. [http://www.nature.com/nrn/journal/v1/n2/full/nrn1100_125a_fs.html link]<br />
<br />
* <big>&#10004;</big> E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]<br />
<br />
: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures" [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]<br />
<br />
* Olshausen BA, Field DJ. Sparse Coding of Sensory Inputs. Curr Op in Neurobiology, 14: 481-487 (2004). [http://redwood.psych.cornell.edu/papers/current-opinion.pdf link]<br />
<br />
* Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103–115 (1990). [http://www.princeton.edu/~wbialek/our_papers/bialek+zee_90.pdf link]<br />
<br />
* Selection from: Spikes: Exploring the Neural Code. F Rieke, D Warland, R de Ruyter van Steveninck & W Bialek (MIT Press, Cambridge, 1997). [http://melvyl.cdlib.org/F/?func=find-b&base=CDL90&request=0-262-18174-6&find_code=020 link]<br />
<br />
* <big>&#10004;</big> H. Nover, C. H. Anderson, and G. C. DeAngelis, A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance, J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/reprint/25/43/10049 link]<br />
<br />
* <big>&#10004;</big> Ray Singh and Chris Eliasmith, Higher-Dimensional Neurons Explain the Tuning and Dynamics of Working Memory Cells, J. Neurosci. 2006;26 3667-3678 [http://www.jneurosci.org/cgi/reprint/26/14/3667 link]<br />
<br />
==== Cortical Microcircuit/Universal Cortical Algorithm ====<br />
<br />
* <big>&#10004;</big> Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press [http://redwood.berkeley.edu/~amir/pdf/Mountcastle78.pdf link]<br />
<br />
* <big>&#10004;</big> Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 [http://arjournals.annualreviews.org/doi/full/10.1146/annurev.neuro.27.070203.144152 link]<br />
<br />
* <big>&#10004;</big> Selection from: Hawkins, J., On Intelligence (Chapter 6) [http://redwood.berkeley.edu/~amir/pdf/HawkinsChap6.pdf link]<br />
<br />
* <big>&#10004;</big> Henry Markram, The Blue Brain Project, Nature Neuroscience, 7:153-160, 2006. [http://www.nature.com/nrn/journal/v7/n2/pdf/nrn1848.pdf link]<br />
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* Douglas RJ, Martin KAC Whitteridge D. (1989) A canonical microcircuit for neocortex. Neural Computation 1: 480-488. [http://scholar.google.com/scholar?hl=en&lr=&q=douglas+martin+whitteridge+neural+comput+1989&btnG=Search link] (the [http://www.archive.org/details/redwood_center_inaugural_symposium_08 related talk] at the Redwood symposium)<br />
<br />
* Cross-modal plasticity in cortical development: differentiation and specification sensory cortex, by Mriganka Sur, Sarah L. Pallas and Anna W. Roe. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1694329&dopt=Citation link]<br />
<br />
* Poggio, T. and E. Bizzi. Generalization in Vision and Motor Control, Nature, Vol. 431, 768-774, 2004. [http://www.nature.com/nature/journal/v431/n7010/abs/nature03014.html link]<br />
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* Marr D, "A Theory for Cerebral Neocortex", Proc Roy Soc London(B), 176, 161-234, 1970. [http://adsabs.harvard.edu/abs/1970RSPSB.176..161M link]<br />
<br />
==== Feedback, Hierarchical Organization, Generative Models ====<br />
<br />
* <big>&#10004;</big> David Mumford, Neuronal Architectures for Pattern-theoretic Problems, in "Large-Scale Neuronal Theories of the Brain", C.Koch & J.Davis, editors, MIT Press, 1994, pp.125-152. [http://redwood.berkeley.edu/~amir/pdf/Neuronal_Mumford.pdf link]<br />
<br />
* <big>&#10004;</big> RPN Rao. Bayesian inference and attention in the visual cortex. Neuroreport 16(16), 1843-1848, 2005. [http://www.cs.washington.edu/homes/rao/nreport_bayes_atten05.pdf link]<br />
<br />
* TS Lee, D Mumford Hierarchical Bayesian inference in the visual cortex, Journal of the Optical Society of America A, 2003 [http://scholar.google.com/url?sa=U&q=http://www.dam.brown.edu/people/mumford/Papers/JOSALeeMumford.pdf link]<br />
<br />
* Felleman, DJ and Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1822724&dopt=Abstract link]<br />
<br />
==== Manifold Learning ====<br />
<br />
* <big>&#10004;</big> Richard Durbin & Graeme Mitchison, A dimension reduction framework for understanding cortical maps, Nature 343, 644 - 647 (15 February 1990) [http://www.nature.com/nature/journal/v343/n6259/abs/343644a0.html link]<br />
<br />
* <big>&#10004;</big> Tal Kenet, Dmitri Bibitchkov, Misha Tsodyks, Amiram Grinvald and Amos Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425, 954-956 (30 October 2003) [http://www.nature.com/nature/journal/v425/n6961/full/nature02078.html link]<br />
<br />
* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994 [http://citeseer.ist.psu.edu/context/2930/0 link]<br />
<br />
* <big>&#10004;</big> JB Tenenbaum, V de Silva, JC Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2319 link]<br />
<br />
* <big>&#10004;</big> ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2323 link]<br />
<br />
* Nicholas V. Swindale, Doron Shoham, Amiram Grinvald, Tobias Bonhoeffer & Mark Hübener, Visual cortex maps are optimized for uniform coverage, Nature Neuroscience 3, 822 - 826 (2000) [http://www.nature.com/neuro/journal/v3/n8/full/nn0800_822.html link]<br />
<br />
Background reading on SOMs<br />
* Neural Computation and Self-Organizing Maps - An Introduction, by Helge Ritter, Thomas Martinetz, and Klaus Schulten, Addison-Wesley, New York, 1992, [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_4.pdf Kohonen's Network Model] [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_book.html Contents]<br />
<br />
==== Plasticity, Hebbian Learning ====<br />
<br />
* Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30 [http://www.ling.sinica.edu.tw/paper-Mu-ming%2520Poo-2.pdf link]<br />
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* Abbott LF, Nelson SB. (2000) Synaptic plasticity: taming the beast. Nat Neurosci. 3 Suppl:1178-83. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11127835&dopt=Abstract link]<br />
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* P. Foldiak, Forming sparse representations by local anti-Hebbian learning, Biological Cybernetics, vol. 64, pp. 165-170, 1990. [http://www.st-andrews.ac.uk/~pf2/FoldiakSparseBC90.pdf link]<br />
<br />
* M. Tsodyks, Spike-timing-dependent synaptic plasticity–The long road towards understanding neuronal mechanisms Trends in Neuroscience, 2002. [http://dx.doi.org/10.1016/S0166-2236(02)02294-4 link]<br />
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* Saudargienne A, Porr B, and Worgotter F. How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp 16: 595–625, 2004. [http://neco.mitpress.org/cgi/content/abstract/16/3/595 link]<br />
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* Seung, HS (2000) Half a century of Hebb. Nat. Neurosc. Suppl: 1166. [http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v3/n11s/full/nn1100_1166.html link] <br />
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* H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063-1073 (2003). [http://hebb.mit.edu/people/seung/papers/Neuron18Dec03.pdf link]<br />
<br />
* Hinton, G. E. and Sejnowski, T. J. (1986), Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104291 link]<br />
<br />
==== Oscillations ====<br />
<br />
* Gray, CM (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron. 1999, 24(1):31-47, 111-25. Review. [http://scholar.google.com/url?sa=U&q=http://lifesci.rutgers.edu/~auerbach/Gray%2520binding.pdf link]<br />
<br />
* Neuron Special Issue on Oscillations, 1999, 24(1) [http://www.sciencedirect.com/science?_ob=IssueURL&_tockey=%23TOC%237054%231999%23999759998%23575347%23FLA%23&_auth=y&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=fcf1d593bba077aaec2c4f34adbe071d link]<br />
<br />
==== Associative Memory ====<br />
<br />
* <big>&#10004;</big> J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, 79:2554-2558 (1982) [http://www.pnas.org/cgi/content/abstract/79/8/2554 link]<br />
** David MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge Univ. Press, 2003. [http://www.inference.phy.cam.ac.uk/mackay/itprnn/ps/504.520.pdf Chapter 42 (Hopfield networks)]<br />
<br />
* <big>&#10004;</big> S. Fusi, P.J. Drew, and L.F. Abbott, Cascade Models of Synaptically Stored Memories, Neuron 2005 45: 599-611. [http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WSS-4FH5K0X-H-1&_cdi=7054&_user=4420&_orig=search&_coverDate=02%2F17%2F2005&_qd=1&_sk=999549995&view=c&wchp=dGLbVlz-zSkWz&md5=1b45b90c5fb16f945ba490ba48503de7&ie=/sdarticle.pdf link]<br />
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* G. Palm. On the storage capacity of an associative memory with randomly distributed storage elements. Biol. Cybernetics. 36:19-31 (1980). [http://www.springerlink.com/link.asp?id=wp7q375127415311 link]<br />
<br />
* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994<br />
<br />
==== Models of Invariance ====<br />
<br />
* Olshausen BA, Anderson CH, Van Essen DC (1993). A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information, The Journal of Neuroscience, 13(11), 4700-4719. [http://redwood.berkeley.edu/bruno/papers/ link]<br />
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* K Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics (Historical Archive), Volume 36, Issue 4, Apr 1980, Pages 193 - 202 [http://www.springerlink.com/link.asp?id=r6g5w3tt54528137 link]<br />
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* <big>&#10004;</big> P. Foldiak, Learning invariance from transformation sequences, Neural Computation, vol. 3, pp. 194-200, 1991. [http://www.st-andrews.ac.uk/~pf2/FoldiakInvarianceLearningNC91.pdf link]<br />
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* <big>&#10004;</big> L Wiskott. Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation. 2002;14:715-770. [http://redwood.berkeley.edu/~cadieu/pdf/WiskottSejnowski-SlowFeature.pdf link]<br />
<br />
==== Active Perception-sensorimotor loops ====<br />
<br />
* <big>&#10004;</big> D. Philipona, J.K.O'Regan & J.-P. Nadal. Is there something out there? Inferring space from sensorimotor dependencies. Neural Computation, 2003, 15, 9, 2029-2050. [http://nivea.psycho.univ-paris5.fr/Philipona/space.pdf link]<br />
<br />
* <big>&#10004;</big> Philipona, D., O Regan, J.K., Nadal, J.-P. and Coenen, O. J.-M. D. Perception of the structure of the physical world using multimodal unknown sensors and effectors. Advances in Neural Information Processing Systems, 16, 2004. [http://www.csl.sony.fr/downloads/papers/2004/philipona-04b.pdf link]<br />
<br />
* Churchland P, Ramachandran VS, Sejnowski TJ. Large-Scale Neuronal Theories of the Brain: Chapter 2, Critique of Pure Vision, 1994. [http://cnl.salk.edu/~terry/PDF/PureVision.94.pdf link]<br />
<br />
* Oregan JK, Noe A. A sensorimotor account of vision and visual consciousness, BBS (2001) 24:939-1031. [http://ist-socrates.berkeley.edu/~noe/oregan.noe.pdf link]<br />
<br />
====Birdsong====<br />
<br />
* <big>&#10004;</big> Anthony Leonardo & Michale S. Fee. Ensemble Coding of Vocal Control in Birdsong. The Journal of Neuroscience, January 19, 2005, 25(3):652-661. [http://www.jneurosci.org/cgi/reprint/25/3/652 link (Warning: 27.7 MB .pdf)]<br />
<br />
* <big>&#10004;</big> Troyer TW, Bottjer SW. Birdsong: models and mechanisms. Curr Opin Neurobiol. 2001 Dec;11(6):721-6. [http://redwood.berkeley.edu/~cadieu/pdf/TroyerBottjer_Birdsong_CurrOpinNeuro01.pdf link]<br />
<br />
==== Theories of the Ventral Stream ====<br />
<br />
* <big>&#10004;</big> Ullman, S. (1995). Sequence-seeking and counter streams: A computational model for bi-directional information flow in the visual cortex. Cerebral Cortex, 5(1) 1-11. [http://redwood.berkeley.edu/~cadieu/pdf/UllmanCerCor95.pdf link]<br />
<br />
* <big>&#10004;</big> Riesenhuber, M., and T. Poggio. Models of Object Recognition, Nature Neuroscience, 3 Supp., 1199-1204, 2000. [http://www.nature.com/neuro/journal/v3/n11s/pdf/nn1100_1199.pdf link]<br />
<br />
* Riesenhuber, M. and T. Poggio. How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003. [http://cbcl.mit.edu/projects/cbcl/publications/ps/max-vis-cortex-03.pdf link]<br />
<br />
* Rolls,E.T. (1997) A neurophysiological and computational approach to the functions of the temporal lobe cortical visual areas in invariant object recognition. Chapter 9, pp. 184-220 in Computational and Psychophysical Mechanisms of Visual Coding, eds. M.Jenkin and L.Harris. Cambridge University Press: Cambridge. [http://www.cns.ox.ac.uk/publications.html link]<br />
<br />
====Sleep====<br />
* <big>&#10004;</big> Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114. [http://redwood.berkeley.edu/~cadieu/pdf/CrickMitchisonDreamSleep1983.pdf link]<br />
<br />
* <big>&#10004;</big> Sutherland GR, McNaughton B., Memory trace reactivation in hippocampal and neocortical neuronal ensembles., Curr Opin Neurobiol. 2000 Apr;10(2):180-6 [http://redwood.berkeley.edu/~cadieu/pdf/SutherlandMcNaughtonMemoryReactivation1999.pdf link]<br />
<br />
==== Theories of Hippocampus ====<br />
<br />
* Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738. [http://www.science.mcmaster.ca/Psychology/sb.html link]<br />
<br />
* Leutgeb, S., Leutgeb, J.K., Moser, M.-B., and Moser, E.I. (2005). Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15, 738-746. [http://www.cbm.ntnu.no/publications link]<br />
<br />
* [http://www.bris.ac.uk/synaptic/research/projects/memory/spatialmem.htm place cells]<br />
<br />
* Hasselmo, M.E. and McClelland, J.L. (1999) Neural models of memory. Curr. Opinion Neurobiol. 9: 184-188. [http://people.bu.edu/hasselmo/HasselmoMcClelland.pdf link]<br />
<br />
==== Motor System ====<br />
<br />
* Körding, KP. and Wolpert, D. (2004) Bayesian Integration in Sensorimotor Learning, Nature 427:244-247. [http://www.koerding.com/pubs/koerdingNature2004.pdf link]<br />
<br />
* <big>&#10004;</big> Georgopoulos AP. Neuron. 1994 Aug;13(2):257-68. New concepts in generation of movement. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8060612&dopt=Abstract link]<br />
<br />
* <big>&#10004;</big> Todorov E Direct cortical control of muscle activation in voluntary arm movements: a model. (2000) Nature Neuroscience 3(4): 391-398 [http://cogsci.ucsd.edu/~todorov/papers/mi.pdf link]<br />
<br />
* Todorov E. On the role of primary motor cortex in arm movement control (2003) In Progress in Motor Control III, ch 6, pp 125-166, Latash and Levin (eds), Human Kinetics [http://cogsci.ucsd.edu/~todorov/papers/MotorCortex.pdf link]<br />
<br />
* Scott, S.H. (2004) Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5:532-546. [http://limb.biomed.queensu.ca/publications/optimal_feedback_control_and_the_neural_basis.pdf link]<br />
<br />
==== Dynamics ====<br />
<br />
See [[Dynamics Reading Club]] site</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8500TCN2016-03-31T00:06:57Z<p>Dpaiton: /* Topics in Computational Neuroscience */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
[Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8499TCN2016-03-31T00:04:58Z<p>Dpaiton: /* Fall 2015 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
[Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8498TCN2016-03-30T23:54:46Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]<br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8495TCN2016-03-24T00:03:04Z<p>Dpaiton: /* Topics in Computational Neuroscience */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]<br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8494TCN2016-03-21T18:30:26Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8493TCN2016-03-21T18:30:12Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]<br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015) - Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8491Seminars2016-03-16T17:53:12Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 30, 2016'''<br />
* Tony Zador HWNI job talk at 12:00<br />
<br />
'''April 11, 2016'''<br />
* Speaker: Hao Su<br />
* Time: at 12:00<br />
* Affiliation: Geometric Computing Lab and Artificial Intelligence Lab, Stanford University<br />
* Host: Yubei<br />
* Status: confirmed<br />
* Title: [Tentative] Joint Analysis for 2D Images and 3D shapes<br />
* Abstract: Coming<br />
<br />
'''April 13, 2016'''<br />
* Bence Olveczky - HWNI job talk at 12:00<br />
<br />
'''May 04, 2016'''<br />
* Speaker: Zhengya Zhang<br />
* Time: 12:00<br />
* Affiliation: Electrical Engineering and Computer Science, University of Michigan<br />
* Host: Dylan, Bruno<br />
* Status: Confirmed<br />
* Title:<br />
* Abstract:<br />
<br />
<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Time: 12:00<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8489TCN2016-03-10T22:57:58Z<p>Dpaiton: /* Topics in Computational Neuroscience */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015) - Rhythms for Cognition- Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]<br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8484TCN2016-03-03T00:39:58Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]<br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=Seminars&diff=8483Seminars2016-02-29T18:40:18Z<p>Dpaiton: /* Tentative / Confirmed Speakers */</p>
<hr />
<div>== Instructions ==<br />
<br />
# Check the internal calendar (here) for a free seminar slot. Seminars are usually Wednesdays at noon, but it is flexible in case there is a day that works better for the speaker. However, it is usually best to avoid booking multiple speakers in the same week - it leads to "seminar burnout" and reduced attendance. But use your own judgement here - if its a good opportunity and that's the only time that works then go ahead with it.<br />
# Once you have proposed a date to a speaker, fill in the speaker information under the appropriate date (or change if necessary). Use the status field to indicate whether the date is tentative or confirmed. Please also include your name as ''host'' in case somebody wants to contact you.<br />
# Once the invitation is confirmed with the speaker, change the status field to 'confirmed'. Also notify the webmaster (Bruno) [mailto:baolshausen@berkeley.edu] that we have a confirmed speaker so that he/she can update the public web page. Please include a title and abstract.<br />
# Natalie (HWNI) checks our web page regularly and will send out an announcement a week before and also include with the weekly neuro announcements, but if you don't get it confirmed until the last minute then make sure to email Natalie [mailto:nrterranova@berkeley.edu] as well to give her a heads up so she knows to send out an announcement in time.<br />
# If the speaker needs accommodations you should contact Natalie [mailto:nrterranova@berkeley.edu] to reserve a room at the faculty club. Tell her its for a Redwood speaker so she knows how to bill it.<br />
# During the visit you will need to look after the visitor, schedule visits with other labs, make plans for lunch, dinner, etc., and introduce at the seminar (don't ask Bruno to do this at the last moment). Save receipts for any meals you paid for.<br />
# After the seminar and before the speaker leaves, make sure to give them Natalie's contact info and have them email her their receipts, explaining its for reimbursement for a Redwood seminar. Natalie will then process the reimbursement. She can also help you with getting reimbursed for any expenses you incurred for meals and entertainment.<br />
<br />
== Tentative / Confirmed Speakers ==<br />
<br />
<br />
'''Feb 24, 2016'''<br />
* Speaker: Miguel Perpinan<br />
* Affiliation: UC Merced<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
<br />
'''Mar 1, 2016'''<br />
* Speaker: Leon Gatys<br />
* Affiliation: Univ Tubingen<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 7-9, 2016'''<br />
* NICE workshop<br />
<br />
'''Mar 9, 2016'''<br />
* Tatiana Engel - HWNI job talk at 12:00<br />
<br />
'''Mar 16, 2016'''<br />
* Talia Lerner - HWNI job talk at 12:00<br />
<br />
'''Mar 23, 2016'''<br />
* Speaker: Kwabena Boahen<br />
* Affiliation: Stanford<br />
* Host: Max Kanwal/Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Mar 30, 2016'''<br />
* Tony Zador HWNI job talk at 12:00<br />
<br />
'''April 13, 2016'''<br />
* Bence Olveczky - HWNI job talk at 12:00<br />
<br />
'''May 18, 2016'''<br />
* Speaker: Melanie Mitchell<br />
* Affiliation: Portland State University and Santa Fe Institute<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Using Analogy to Recognize Visual Situations<br />
* Abstract: Enabling computers to recognize abstract visual situations remains a hard open problems in artificial intelligence. No machine vision system comes close to matching human ability at identifying the contents of images or visual scenes, or at recognizing abstract similarity between different scenes, even though such abilities pervade human cognition. In this talk I will describe my research on getting computers to flexibly recognize visual situations by integrating low-level vision algorithms with an agent-based model of higher-level concepts and analogy-making. <br />
* Bio: Melanie Mitchell is Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute. She received a Ph.D. in Computer Science from the University of Michigan. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies. She is the author or editor of five books and over 70 scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her most recent book, Complexity: A Guided Tour (Oxford, 2009), won the 2010 Phi Beta Kappa Science Book Award. It was also named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie directs the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.<br />
<br />
== Previous Seminars ==<br />
<br />
=== 2015/16 academic year ===<br />
<br />
'''July 21, 2015'''<br />
* Speaker: Felix Effenberger<br />
* Affiliation: <br />
* Host: Chris H.<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 22, 2015'''<br />
* Speaker: Lav Varshney<br />
* Affiliation: Urbana-Champaign<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 23, 2015'''<br />
* Speaker: Xuemin Wei<br />
* Affiliation: Univ Penn<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''July 29, 2015'''<br />
* Speaker: Gonzalo Otazu<br />
* Affiliation: Cold Spring Harbor Laboratory, Long Island, NY<br />
* Host: Mike D<br />
* Status: Confirmed<br />
* Title: The Role of Cortical Feedback in Olfactory Processing<br />
* Abstract: The olfactory bulb receives rich glutamatergic projections from the piriform cortex. However, the dynamics and importance of these feedback signals remain unknown. In the first part of this talk, I will present data from multiphoton calcium imaging of cortical feedback in the olfactory bulb of awake mice. Responses of feedback boutons were sparse, odor specific, and often outlasted stimuli by several seconds. Odor presentation either enhanced or suppressed the activity of boutons. However, any given bouton responded with stereotypic polarity across multiple odors, preferring either enhancement or suppression. Inactivation of piriform cortex increased odor responsiveness and pairwise similarity of mitral cells but had little impact on tufted cells. We propose that cortical feedback differentially impacts these two output channels of the bulb by specifically decorrelating mitral cell responses to enable odor separation. In the second part of the talk I will introduce a computational model of odor identification in natural scenes that uses cortical feedback and how the model predictions match our experimental data.<br />
<br />
'''Aug 19, 2015'''<br />
* Speaker: Wujie Zhang<br />
* Affiliation: Columbia<br />
* Host: Bruno/Michael Yartsev<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''Sept 2, 2015'''<br />
* Speaker: Jeremy Maitin-Shepard<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Combinatorial Energy Learning for Image Segmentation<br />
* Abstract: Recent advances in volume electron microscopy make it possible to image neuronal tissue volumes containining hundreds of thousands of neurons at sufficient resolution to discern even the finest neuronal processes. Accurate 3-D segmentation of these processes densely packed in these petavoxel-scale volumes is the key bottleneck in reconstructing large-scale neural circuits.<br />
<br />
'''Sept 8, 2015'''<br />
* Speaker: Jennifer Hasler<br />
* Affiliation: Georgia Tech<br />
* Host: Bruno/Mika<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''October 29, 2015'''<br />
* Speaker: Garrett Kenyon<br />
* Affiliation: Los Alamos National Laboratory<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: A Deconvolutional Competitive Algorithm (DCA)<br />
* Abstract: The Locally Competitive Algorithm (LCA) is a neurally-plausible sparse solver based on lateral inhibition between leaky integrator neurons. LCA accounts for many linear and nonlinear response properties of V1 simple cells, including end-stopping and contrast-invariant orientation tuning. Here, we describe a convolutional implementation of LCA in which a column of feature vectors is replicated with a stride that is much smaller than the diameter of the corresponding kernels, allowing the construction of dictionaries that are many times more overcomplete than without replication. Using a local Hebbian rule that minimizes sparse reconstruction error, we are able to learn representations from unlabeled imagery, including monocular and stereo video streams, that in some cases support near state-of-the-art performance on object detection, action classification and depth estimation tasks, with a simple linear classifier. We further describe a scalable approach to building a hierarchy of convolutional LCA layers, which we call a Deconvolutional Competitive Algorithm (DCA). All layers in a DCA are trained simultaneously and all layers contribute to a single image reconstruction, with each layer deconvolving its representation through all lower layers back to the image plane. We show that a 3-layer DCA trained on short video clips obtained from hand-held cameras exhibits a clear segregation of image content, with features in the top layer reconstructing large-scale structures while features in the middle and bottom layers reconstruct progressively finer details. Lastly, we describe PetaVision, an open source, cloud-friendly, high-performance neural simulation toolbox that was used to perform the numerical studies presented here.<br />
<br />
'''Nov 18, 2015'''<br />
* Speaker: Hillel Adesnik<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Nov 17, 2015'''<br />
* Speaker: Manuel Lopez<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract<br />
<br />
'''Dec 2, 2015'''<br />
* Speaker: Steven Brumby<br />
* Affiliation: [http://www.descarteslabs.com/ Descartes Labs]<br />
* Host: Dylan<br />
* Status: confirmed<br />
* Title: Seeing the Earth in the Cloud<br />
* Abstract: The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte scale (8×10^15 bits) are now available in commercial clouds, and new satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in neuroscience-inspired machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. <br />
<br />
'''Dec 14, 2015'''<br />
* Speaker: Bill Softky <br />
* Affiliation:<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Screen addition - informal Redwood group seminar<br />
<br />
'''Dec 16, 2015'''<br />
* Speaker: Mike Landy<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 3, 2016'''<br />
* Speaker: Ping-Chen Huang<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title:<br />
<br />
'''Feb 17, 2016'''<br />
* Speaker: Andrew Saxe<br />
* Affiliation: Harvard<br />
* Host: Jesse<br />
* Status: confirmed<br />
* Title: Hallmarks of Deep Learning in the Brain<br />
<br />
=== 2014/15 academic year ===<br />
<br />
'''2 July 2014'''<br />
* Speaker: Kelly Clancy<br />
* Affiliation: Feldman lab<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Volitional control of neural assemblies in L2/3 of motor and somotosensory cortices<br />
* Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.<br />
<br />
'''23 July 2014'''<br />
* Speaker: Gautam Agarwal<br />
* Affiliation: UC Berkeley/Champalimaud<br />
* Host: Friedrich Sommer<br />
* Status: confirmed<br />
* Title: Unsolved Mysteries of Hippocampal Dynamics<br />
* Abstract: Two radically different forms of electrical activity can be observed in the rat hippocampus: spikes and local field potentials (LFPs). Hippocampal pyramidal neurons are mostly silent, yet spike vigorously as the subject encounters particular locations in its environment. In contrast, LFPs appear to lack place-selectivity, persisting regardless of the rat's location. Recently, we found that in fact one can recover from LFPs the spatial information present in the underlying neuronal population, showing how these two signals are two sides of the same coin. Nonetheless, there are many aspects of the LFP that remain mysterious. I will review several observations and explanatory gaps which await further study. These include: the relationship of LFP patterns to anatomy; the elusive structure of gamma waves; complex forms of cross-frequency coupling; variations in LFP patterns seen when the rat explores its world more freely; reconciling the memory and navigation roles of the hippocampus.<br />
<br />
'''6 Aug 2014'''<br />
* Speaker: Georg Martius<br />
* Affiliation: Max Planck Institute, Leipzig<br />
* Host: Fritz Sommer<br />
* Status: confirmed<br />
* Title: Information driven self-organization of robotic behavior<br />
* Abstract: Autonomy is a puzzling phenomenon in nature and a major challenge in the world of artifacts. A key feature of autonomy in both natural and<br />
artificial systems is seen in the ability for independent<br />
exploration. In animals and humans, the ability to modify its own<br />
pattern of activity is not only an indispensable trait for adaptation<br />
and survival in new situations, it also provides a learning system<br />
with novel information for improving its cognitive capabilities, and<br />
it is essential for development. Efficient exploration in<br />
high-dimensional spaces is a major challenge in building learning<br />
systems. We propose to implement the exploration as a deterministic<br />
law derived from maximizing an information quantity. More<br />
specifically we use the predictive information of the sensor process<br />
(of a robot) to obtain an update rule (exploration dynamics) of the<br />
controller parameters. To be adequate in robotics application the<br />
non-stationary nature of the underlying time-series have to be taken<br />
into account, which we do by proposing the time-local predictive<br />
information (TiPI). Importantly the exploration dynamics is derived<br />
analytically and by this we link information theory and dynamical<br />
systems. Without a random component the change in the parameters is<br />
deterministically given as a function of the states in a certain time<br />
window. For an embodied system this means in particular that<br />
constraints, responses and current knowledge of the dynamical<br />
interaction with the environment can directly be used to advance<br />
further exploration. Randomness is replaced with spontaneity which we<br />
demonstrate to restrict the search space automatically to the<br />
physically relevant dimensions. Its effectiveness will be<br />
presented with various experiments on high-dimensional robotic system<br />
and we argue that this is a promising way to avoid the curse of<br />
dimensionality. This talk describes joint work with Ralf Der and Nihat Ay.<br />
<br />
'''15 Aug 2014'''<br />
* Speaker: Juergen Schmidhuber<br />
* Affiliation: IDSIA, Switzerland<br />
* Host: James/Shariq<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''2 Sept 2014'''<br />
* Speaker: Oriol Vinyals <br />
* Affliciation: Google<br />
* Host: Guy<br />
* Status: confirmed<br />
* Title: Machine Translation with Long-Short Term Memory Models<br />
* Abstract: Supervised large deep neural networks achieved good results on speech recognition and computer vision. Although very successful, deep neural networks can only be applied to problems whose inputs and outputs can be conveniently encoded with vectors of fixed dimensionality - but cannot easily be applied to problems whose inputs and outputs are sequences. In this work, we show how to use a large deep Long Short-Term Memory (LSTM) model to solve domain-agnostic supervised sequence to sequence problems with minimal manual engineering. Our model uses one LSTM to map the input sequence to a vector of a fixed dimensionality and another LSTM to map the vector to the output sequence. We applied our model to a machine translation task and achieved encouraging results. On the WMT'14 translation task from English to French, a model combination of 6 large LSTMs achieves a BLEU score of 32.3 (where a larger score is better). For comparison, a strong standard statistical MT baseline achieves a BLEU score of 33.3. When we use our LSTM to rescore the n-best lists produced by the SMT baseline, we achieve a BLEU score of 36.3, which is a new state of the art. This is joint work with Ilya Sutskever and Quoc Le.<br />
<br />
'''19 Sept 2014'''<br />
* Speaker: Gary Marcus<br />
* Affiliation: NYU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''24 Sept 2014'''<br />
* Speaker: Alyosha Efros<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''30 Sep 2014'''<br />
* Speaker: Alejandro Bujan<br />
* Affiliation:<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Propagation and variability of evoked responses: the role of correlated inputs and oscillations<br />
* Abstract: <br />
<br />
'''8 Oct 2014'''<br />
* Speaker: Siyu Zhang<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: confirmed<br />
* Title: Long-range and local circuits for top-down modulation of visual cortical processing<br />
* Abstract:<br />
<br />
'''15 Oct 2014'''<br />
* Speaker: Tamara Broderick<br />
* Affiliation: UC Berkeley<br />
* Host: Yvonne/James<br />
* Status: confirmed<br />
* Title: Feature allocations, probability functions, and paintboxes<br />
* Abstract: Clustering involves placing entities into mutually exclusive categories. We wish to relax the requirement of mutual exclusivity, allowing objects to belong simultaneously to multiple classes, a formulation that we refer to as "feature allocation." The first step is a theoretical one. In the case of clustering the class of probability distributions over exchangeable partitions of a dataset has been characterized (via exchangeable partition probability functions and the Kingman paintbox). These characterizations support an elegant nonparametric Bayesian framework for clustering in which the number of clusters is not assumed to be known a priori. We establish an analogous characterization for feature allocation; we define notions of "exchangeable feature probability functions" and "feature paintboxes" that lead to a Bayesian framework that does not require the number of features to be fixed a priori. The second step is a computational one. Rather than appealing to Markov chain Monte Carlo for Bayesian inference, we develop a method to transform Bayesian methods for feature allocation (and other latent structure problems) into optimization problems with objective functions analogous to K-means in the clustering setting. These yield approximations to Bayesian inference that are scalable to large inference problems.<br />
<br />
'''29 Oct 2014'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Topics in higher level visuo-motor control<br />
* Abstract: TBA<br />
<br />
'''5 Nov 2014''' - **BVLC retreat**<br />
<br />
'''20 Nov 2014'''<br />
* Speaker: Haruo Hasoya<br />
* Affiliation: ATR Institute, Japan<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''9 Dec 2014'''<br />
* Speaker: Dirk DeRidder<br />
* Affiliation: Dundedin School of Medicine, University of Otago, New Zealand<br />
* Host: Bruno/Walter Freeman<br />
* Status: confirmed<br />
* Title: The Bayesian brain, phantom percepts and brain implants<br />
* Abstract: TBA<br />
<br />
'''January 14, 2015'''<br />
* Speaker: Kevin O'regan<br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 21, 2015'''<br />
* Speaker: Adrienne Fairhall<br />
* Affiliation: University of Washington<br />
* Host: Mike Schachter<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''January 26, 2015'''<br />
* Speaker: Abraham Peled<br />
* Affiliation: Mental Health Center, 'Technion' Israel Institute of Technology<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Clinical Brain Profiling: A Neuro-Computational psychiatry<br />
* Abstract: TBA<br />
<br />
'''January 28, 2015'''<br />
* Speaker: Rich Ivry<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Embodied Decision Making: System interactions in sensorimotor adaptation and reinforcement learning<br />
* Abstract:<br />
<br />
'''February 11, 2015'''<br />
* Speaker: Mark Lescroart<br />
* Affiliation: UC Berkeley<br />
* Host: Karl<br />
* Status: tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''February 25, 2015'''<br />
* Speaker: Steve Chase<br />
* Affiliation: CMU<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Joint Redwood/CNEP seminar<br />
* Abstract:<br />
<br />
'''March 3, 2015'''<br />
* Speaker: Andreas Herz<br />
* Affiliation: Bernstein Center, Munich<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''March 3, 2015 - 4:00'''<br />
* Speaker: James Cooke<br />
* Affiliation: Oxford<br />
* Host: Mike Deweese<br />
* Status: confirmed<br />
* Title: Neural Circuitry Underlying Contrast Gain Control in Primary Auditory Cortex<br />
* Abstract:<br />
<br />
'''March 4, 2015'''<br />
* Speaker: Bill Sprague<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: V1 disparity tuning and the statistics of disparity in natural viewing<br />
* Abstract:<br />
<br />
'''March 11, 2015'''<br />
* Speaker: Jozsef Fiser<br />
* Affiliation: Central European University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 1, 2015'''<br />
* Speaker: Saeed Saremi<br />
* Affiliation: Salk Inst<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''April 15, 2015'''<br />
* Speaker: Zahra M. Aghajan<br />
* Affiliation: UCLA<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hippocampal Activity in Real and Virtual Environments<br />
* Abstract:<br />
<br />
'''May 7, 2015'''<br />
* Speaker: Santani Teng<br />
* Affiliation: MIT<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''May 13, 2015'''<br />
* Speaker: Harri Valpola<br />
* Affiliation: ZenRobotics<br />
* Host: Brian<br />
* Status: Tentative<br />
* Title: TBA<br />
* Abstract<br />
<br />
'''June 24, 2015'''<br />
* Speaker: Kendrick Kay<br />
* Affiliation: Department of Psychology, Washington University in St. Louis<br />
* Host: Karl<br />
* Status: Confirmed<br />
* Title: Using functional neuroimaging to reveal the computations performed by the human visual system<br />
* Abstract<br />
Visual perception is the result of a complex set of computational transformations performed by neurons in the visual system. Functional magnetic resonance imaging (fMRI) is ideally suited for identifying these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. In this talk, I will review past research in which we used fMRI to develop increasingly accurate models of the stimulus transformations occurring in early and intermediate visual areas. I will then describe recent research in which we successfully extend this approach to high-level visual areas involved in perception of visual categories (e.g. faces) and demonstrate how top-down attention modulates bottom-up stimulus representations. Finally, I will discuss ongoing research targeting regions of ventral temporal cortex that are essential for skilled reading. Our model-based approach, combined with high-field laminar measurements, is expected to provide an integrated picture of how bottom-up stimulus transformations and top-down cognitive factors interact to support rapid and accurate word recognition. Development of quantitative models and associated experimental paradigms may help us understand and diagnose impairments in neural processing that underlie visual disorders such as dyslexia and prosopagnosia.<br />
<br />
=== 2013/14 academic year ===<br />
<br />
'''9 Oct 2013'''<br />
* Speaker: Ekaterina Brocke<br />
* Affiliation: KTH University, Stockholm, Sweden<br />
* Host: Tony<br />
* Status: confirmed<br />
* Title: Multiscale modeling in Neuroscience: first steps towards multiscale co-simulation tool development.<br />
* Abstract: Multiscale modeling/simulations attracts an increasing number of neuroscientists to study how different levels of organization (networks of neurons, cellular/subcellular levels) interact with each other across multiple scales, space and time, to mediate different brain functions. Different scales are usually described by different physical and mathematical formalisms thus making it non trivial to perform the integration. In this talk, I will discuss key phenomena in Neuroscience that can be addressed using subcellular/cellular models, possible approaches to perform multiscale simulations in particular a co-simulation method. I will also introduce several multiscale "toy" models of cellular/subcellular levels that were developed with the aim to understand numerical and technical problems which might appear during the co-simulation. And finally, the first steps made towards multiscale co-simulation tool development will be presented during the talk.<br />
<br />
'''29 Oct 2013 - note: 4:00'''<br />
* Speaker: Mitya Chkolovskii<br />
* Affiliation: HHMI/Janelia Farm<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 Oct 2013'''<br />
* Speaker: Ilya Nemanman<br />
* Affiliation: Emory University, Departments of Physics and Biology<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Large N in neural data -- expecting the unexpected.<br />
* Abstract: Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.<br />
<br />
'''31 Oct 2013'''<br />
* Speaker: Oriol Vinyals<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno/Brian<br />
* Status: confirmed<br />
* Title: Beyond Deep Learning: Scalable Methods and Models for Learning<br />
* Abstract: In this talk I will briefly describe several techniques I explored in my thesis that improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology "deep learning"), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research.<br />
<br />
'''6 Nov 2013'''<br />
* Speaker: Garrett T. Kenyon<br />
* Affiliation: Los Alamos National Laboratory, The New Mexico Consortium<br />
* Host: Dylan Paiton<br />
* Status: Confirmed<br />
* Title: Using Locally Competitive Algorithms to Model Top-Down and Lateral Interactions<br />
* Abstract: Cortical connections consist of feedforward, feedback and lateral pathways. Infragranular layers project down the cortical hierarchy to both supra- and infragranular layers at the previous processing level, while the neurons in supragranular layers are linked by extensive long-range lateral projections that cross multiple cortical columns. However, most functional models of visual cortex only account for feedforward connections. Additionally, most models of visual cortex fail to account both for the thalamic projections to non-striate areas and the reciprocal connections from extrastriate areas back to the thalamus. In this talk, I will describe how a modified Locally Competitive Algorithm (LCA; Rozell et al, Neural Comp, 2008) can be used as a unifying framework for exploring the role of top-down and lateral cortical pathways within the context of deep, sparse, generative models. I will also describe an open source software tool called PetaVision that can be used to implement and execute hierarchical LCA-based models on multi-core, multi-node computer platforms without requiring specific knowledge of parallel-programming constructs.<br />
<br />
'''14 Nov 2013 (note: Thursday), ***12:30pm*** '''<br />
* Speaker: Geoffrey J Goodhill<br />
* Affiliation: Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Australia<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Computational principles of neural wiring development<br />
* Abstract: Brain function depends on precise patterns of neural wiring. An axon navigating to its target must make guidance decisions based on noisy information from molecular cues in its environment. I will describe a combination of experimental and computational work showing that (1) axons may act as ideal observers when sensing chemotactic gradients, (2) the complex influence of calcium and cAMP levels on guidance decisions can be predicted mathematically, (3) the morphology of growth cones at the axonal tip can be understood in terms of just a few eigenshapes, and remarkably these shapes oscillate in time with periods ranging from minutes to hours. Together this work may shed light on how neural wiring goes wrong in some developmental brain disorders, and how best to promote appropriate regrowth of axons after injury.<br />
<br />
'''4 Dec 2013'''<br />
* Speaker: Zhenwen Dai<br />
* Affiliation: FIAS, Goethe University Frankfurt, Germany.<br />
* Host: Georgios Exarchakis<br />
* Status: Confirmed<br />
* Title: What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach <br />
* Abstract: We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions. Some earlier models used a separate encoding of features and their positions to facilitate invariant data encoding and recognition. All probabilistic generative models with explicit position encoding have so far assumed a linear superposition of components to encode image patches. Here, we for the first time apply a model with non-linear feature superposition and explicit position encoding for patches. By avoiding linear superpositions, the studied model represents a closer match to component occlusions which are ubiquitous in natural images. In order to account for occlusions, the non-linear model encodes patches qualitatively very different from linear models by using component representations separated into mask and feature parameters. We first investigated encodings learned by the model using artificial data with mutually occluding components. We find that the model extracts the components, and that it can correctly identify the occlusive components with the hidden variables of the model. On natural image patches, the model learns component masks and features for typical image components. By using reverse correlation, we estimate the receptive fields associated with the model’s hidden units. We find many Gabor-like or globular receptive fields as well as fields sensitive to more complex structures. Our results show that probabilistic models that capture occlusions and invariances can be trained efficiently on image patches, and that the resulting encoding represents an alternative model for the neural encoding of images in the primary visual cortex. <br />
<br />
'''11 Dec 2013'''<br />
* Speaker: Kai Siedenburg<br />
* Affiliation: UC Davis, Petr Janata's Lab.<br />
* Host: Jesse Engel<br />
* Status: Confirmed<br />
* Title: Characterizing Short-Term Memory for Musical Timbre<br />
* Abstract: Short-term memory is a cognitive faculty central for the apprehension of music and speech. Only little is known, however, about memory for musical timbre despite its“sisterhood”with speech; after all, speech can be regarded as sequencing of vocal timbre. Past research has isolated many characteristic effects of verbal memory. Are these also in play for non-vocal timbre sequences? We studied this question by considering short-term memory for serial order. Using timbres and dissimilarity data from McAdams et al. (Psych. Research, 1995), we employed a same/different discrimination paradigm. Experiment 1 (N = 30 MU + 30 nonMU) revealed effects of sequence length and timbral dissimilarity of items, as well as an interaction of musical training and pitch variability: in contrast to musicians, non-musicians' performance was impaired by simultaneous changes in pitch, compared to a constant pitch baseline. Experiment 2 (N = 22) studied whether musicians' memory for timbre sequences was independent of pitch irrespective of the degree of complexity of pitch progressions. Comparing sequences with pitch changing within and across standard and comparison to a constant pitch baseline, performance was now clearly impaired for the variable pitch condition. Experiment 3 (N = 22) showed primacy and recency effects for musicians, and reproduced a positive effect of timbral heterogeneity of sequences. Our findings demonstrate the presence of hallmark effects of verbal memory such as similarity, word length, primacy/recency for the domain of non-vocal timbre, and suggest that memory for speech and non- vocal timbre sequences might to a large extent share underlying mechanisms.<br />
<br />
'''12 Dec 2013'''<br />
* Speaker: Matthias Bethge<br />
* Affiliation: University of Tubingen<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 Jan 2014'''<br />
* Speaker: Thomas Martinetz<br />
* Affiliation: Univ Luebeck<br />
* Host: Bruno/Fritz<br />
* Status: confirmed<br />
* Title: Orthogonal Sparse Coding and Sensing<br />
* Abstract: Sparse Coding has been a very successful concept since many natural signals have the property of being sparse in some dictionary (basis). Some natural signals are even sparse in an orthogonal basis, most prominently natural images. They are sparse in a respective wavelet transform. An encoding in an orthogonal basis has a number of advantages,.e.g., finding the optimal coding coefficients is simply a projection instead of being NP-hard.<br />
Given some data, we want to find the orthogonal basis which provides the sparsest code. This problem can be seen as a <br />
generalization of Principal Component Analysis. We present an algorithm, Orthogonal Sparse Coding (OSC), which is able to find this basis very robustly. On natural images, it compresses on the level of JPEG, but can adapt to arbitrary and special data sets and achieve significant improvements. With the property of being sparse in some orthogonal basis, we show how signals can be sensed very efficiently in an hierarchical manner with at most k log D sensing actions. This hierarchical sensing might relate to the way we sense the world, with interesting applications in active vision. <br />
<br />
'''29 Jan 2014'''<br />
* Speaker: David Klein<br />
* Affiliation: Audience<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''5 Feb 2014''' (leave open for Barth/Martinetz seminar)<br />
<br />
'''12 Feb 2014'''<br />
* Speaker: Ilya Sutskever <br />
* Affiliation: Google<br />
* Host: Zayd<br />
* Status: confirmed<br />
* Title: Continuous vector representations for machine translation<br />
* Abstract: Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.<br />
<br />
'''25 Feb 2014'''<br />
* Speaker: Alexander Terekhov <br />
* Affiliation: CNRS - Université Paris Descartes<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Constructing space: how a naive agent can learn spatial relationships by observing sensorimotor contingencies<br />
* Abstract:<br />
<br />
'''12 March 2014'''<br />
* Speaker: Carlos Portera-Cailliau<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: Circuit defects in the neocortex of Fmr1 knockout mice<br />
* Abstract: TBA<br />
<br />
'''19 March 2014'''<br />
* Speaker: Dean Buonomano<br />
* Affiliation: UCLA<br />
* Host: Mike<br />
* Status: confirmed<br />
* Title: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity<br />
* Abstract: The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.<br />
<br />
'''26 March 2014'''<br />
* Speaker: Robert G. Smith<br />
* Affiliation: University of Pennsylvania<br />
* Host: Mike S<br />
* Status: confirmed<br />
* Title: Role of Dendritic Computation in the Direction-Selective Circuit of Retina<br />
* Abstract: The retina utilizes a variety of signal processing mechanisms to compute direction from image motion. The computation is accomplished by a circuit that includes starburst amacrine cells (SBACs), which are GABAergic neurons presynaptic to direction-selective ganglion cells (DSGCs). SBACs are symmetric neurons with several branched dendrites radiating out from the soma. When a stimulus moving back and forth along a SBAC dendrite sequentially activates synaptic inputs, larger post-synaptic potentials (PSPs) are produced in the dendritic tips when the stimulus moves outwards from the soma. The directional difference in EPSP amplitude is further amplified near the dendritic tips by voltage-gated channels to produce directional release of GABA. Reciprocal inhibition between adjacent SBACs may also amplify directional release. Directional signals in the independent SBAC branches are preserved because each dendrite makes selective contacts only with DSGCs of the appropriate preferred-direction. Directional signals are further enhanced within the dendritic arbor of the DSGC, which essentially comprises an array of distinct dendritic compartments. Each of these dendritic compartments locally sum excitatory and inhibitory inputs, amplifies them with voltage-gated channels, and generates spikes that propagate to the axon via the soma. Overall, the computation of direction in the retina is performed by several local dendritic mechanisms both presynaptic and postsynaptic, with the result that directional responses are robust over a broad range of stimuli.<br />
<br />
'''16 April 2014'''<br />
* Speaker: David Pfau<br />
* Affiliation: Columbia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 April 2014 *Tuesday*'''<br />
* Speaker: Jochen Braun<br />
* Affiliation: Otto-von-Guericke University, Magdeburg<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Dynamics of visual perception and collective neural activity<br />
* Abstract:<br />
<br />
'''29 April 2014'''<br />
* Speaker: Guiseppe Vitiello<br />
* Affiliation: University of Salerno<br />
* Host: Fritz/Walter Freeman<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''30 April 2014'''<br />
* Speaker: Masataka Watanabe<br />
* Affiliation: University of Tokyo / Max Planck Institute for Biological Cybernetics<br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Turing Test for Machine Consciousness and the Chaotic Spatiotemporal Fluctuation Hypothesis<br />
* Abstract: I propose an experimental method to test various hypotheses on consciousness. Inspired by Sperry's observation that split-brain patients possess two independent streams of consciousness, the idea is to implement candidate neural mechanisms of visual consciousness onto an artificial cortical hemisphere and test whether subjective experience is evoked in the device's visual hemifield. In contrast to modern neurosynthetic devices, I show that mimicking interhemispheric connectivity assures that authentic and fine-grained subjective experience arises only when a stream of consciousness is generated within the device. It is valid under a widely believed assumption regarding interhemispheric connectivity and neuronal stimulus-invariance. (I will briefly explain my own evidence of human V1 not responding to changes in the contents of visual awareness [1])<br />
<br />
If consciousness is actually generated within the device, we should be able to construct a case where two objects presented in the device's visual field are distinguishable by visual experience but not by what is communicated through the brain-machine interface. As strange as it may sound, and clearly violating the law of physics, this is likely to be happening in the intact brain, where unified subjective bilateral vision and its verbal report occur without the total interhemispheric exchange of conscious visual information.<br />
<br />
Together, I present a hypothesis on the neural mechanism of consciousness, “The Chaotic Spatiotemporal Fluctuation Hypothesis” that passes the proposed test for visual qualia and also explains how physics that we know of today is violated. Here, neural activity is divided into two components, the time-averaged activity and the residual temporally fluctuating activity, where the former serves as the content of consciousness (neuronal population vector) and the latter as consciousness itself. The content is “read” into consciousness in the sense that, every local perturbation caused by change in the neuronal population vector creates a spatiotemporal wave in the fluctuation component that travels through out the system. Deterministic chaos assures that every local difference makes a difference to the whole of the dynamics, as in the butterfly effect, serving as a foundation for the holistic nature of consciousness. I will present data from simultaneous electrophysiology-fMRI recordings and human fMRI [2] that supports the existence of such large-scale causal fluctuation.<br />
<br />
Here, the chaotic fluctuation cannot be decoded to trace back the original perturbation in the neuronal population vector, because initial states of all neurons are required with infinite precision to do so. Hence what is transmitted over the two hemispheres are not "information" in the normal sense. This illustrates the violation of physics by the metaphysical assumption, "chaotic spatiotemporal fluctuation is consciousness", where unification of bilateral vision and the solving of visual tasks (e.g. perfect symmetry detection) are achieved without exchanging the otherwise required Shannon information between the two hemispheres.<br />
<br />
Finally, minimal and realistic versions of the proposed test for visual qualia can be conducted on laboratory animals to validate the hypothesis. It deals with two biological hemispheres, which we know already that it contains consciousness. We dissect interhemispheric connectivity and form instead an artificial one that is capable of filtering out the neural fluctuation component. A limited interhemispheric connectivity may be sufficient, which would drastically discount the technological challenge. If the subject is capable of conducting a bilateral stimuli matching task with the full artificial interhemispheric connectivity, but not when the fluctuation component is filtered out, it can be considered a strong supporting evidence of the hypothesis.<br />
<br />
1.Watanabe, M., Cheng, K., Ueno, K., Asamizuya, T., Tanaka, K., Logothetis, N., Attention but not awareness modulates the BOLD signal in the human V1 during binocular suppression. Science, 2011. 334(6057): p. 829-31.<br />
<br />
2.Watanabe, M., Bartels, A., Macke, J., Logothetis, N., Temporal jitter of the BOLD signal reveals a reliable initial dip and improved spatial resolution. Curr Biol, 2013. 23(21): p. 2146-50.<br />
<br />
'''11 June 2014'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona, Tucson<br />
* Host: Gautam<br />
* Status: confirmed<br />
* Title: ‘Tuning the brain’ – Treating mental states through microtubule vibrations <br />
* Abstract: Do mental states derive entirely from brain neuronal membrane activities? Neuronal interiors are organized by microtubules (‘MTs’), protein polymers proposed to encode memory, process information and support consciousness. Using nanotechnology, Bandyopadhyay’s group at MIT has shown coherent vibrations (megahertz to 10 kilohertz) from microtubule bundles inside active neurons, vibrations (electric field potentials ~40 to 50 mV) able to influence membrane potentials. This suggests EEG rhythms are ‘beat’ frequencies of megahertz vibrations in microtubules inside neurons (Hameroff and Penrose, 2014), and that consciousness and cognition involve vibrational patterns resonating across scales in the brain, more like music than computation. MT megahertz may be a useful therapeutic target for ‘tuning’ mood and mental states. Among noninvasive transcranial brain stimulation techniques (TMS, TDcS), transcranial ultrasound (TUS) is megahertz mechanical vibrations. Applied at the scalp, low intensity, sub-thermal ultrasound (TUS) safely reaches the brain. In human studies, brief (15 to 30 seconds) TUS at 0.5, 2 and 8 megahertz to frontal-temporal cortex results in 40 minutes or longer of reported mood improvement, and focused TUS enhances sensory discrimination (Legon et al, 2014). In vitro, ultrasound promotes growth of neurite outgrowth in embryonic neurons (Raman), and stabilizes microtubules against disassembly (Gupta). (In Alzheimer’s disease, MTs disassemble and release tau.) These findings suggest ‘tuning the brain’ with TUS should be a safe, effective and inexpensive treatment for Alzheimer’s, traumatic brain injury, depression, anxiety, PTSD and other disorders. <br />
<br />
References: Hameroff S, Penrose R (2014) Phys Life Rev http://www.sciencedirect.com/science/article/pii/S1571064513001188; Sahu et al (2013) Biosens Bioelectron 47:141–8; Sahu et al (2013) Appl Phys Lett 102:123701; Legon et al (2014) Nature Neuroscience 17: 322–329<br />
<br />
'''25 June 2014'''<br />
* Speaker: Peter Loxley<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: The two-dimensional Gabor function adapted to natural image statistics: An analytical model of simple-cell responses in the early visual system<br />
* Abstract: TBA<br />
<br />
=== 2012/13 academic year ===<br />
<br />
'''26 Sept 2012''' <br />
* Speaker: Jason Yeatman<br />
* Affiliation: Department of Psychology, Stanford University<br />
* Host: Bruno/Susana Chung<br />
* Status: confirmed<br />
* Title: The Development of White Matter and Reading Skills<br />
* Abstract: The development of cerebral white matter involves both myelination and pruning of axons, and the balance between these two processes may differ between individuals. Cross-sectional measures of white matter development mask the interplay between these active developmental processes and their connection to cognitive development. We followed a cohort of 39 children longitudinally for three years, and measured white matter development and reading development using diffusion tensor imaging and behavioral tests. In the left arcuate and inferior longitudinal fasciculus, children with above-average reading skills initially had low fractional anisotropy (FA) with a steady increase over the 3-year period, while children with below-average reading skills had higher initial FA that declined over time. We describe a dual-process model of white matter development that balances biological processes that have opposing effects on FA, such as axonal myelination and pruning, to explain the pattern of results.<br />
<br />
'''8 Oct 2012''' <br />
* Speaker: Sophie Deneve<br />
* Affiliation: Laboratoire de Neurosciences cognitives, ENS-INSERM<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Balanced spiking networks can implement dynamical systems with predictive coding<br />
* Abstract: Neural networks can integrate sensory information and generate continuously varying outputs, even though individual neurons communicate only with spikes---all-or-none events. Here we show how this can be done efficiently if spikes communicate "prediction errors" between neurons. We focus on the implementation of linear dynamical systems and derive a spiking network model from a single optimization principle. Our model naturally accounts for two puzzling aspects of cortex. First, it provides a rationale for the tight balance and correlations between excitation and inhibition. Second, it predicts asynchronous and irregular firing as a consequence of predictive population coding, even in the limit of vanishing noise. We show that our spiking networks have error-correcting properties that make them far more accurate and robust than comparable rate models. Our approach suggests spike times do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly under-estimated.<br />
<br />
<br />
'''19 Oct 2012'''<br />
* Speaker: Gert Van Dijck<br />
* Affiliation: Cambridge<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: A solution to identifying neurones using extracellular activity in awake animals: a probabilistic machine-learning approach<br />
* Abstract: Electrophysiological studies over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cortical neurones. Previous studies have employed a variety of measures based on spike timing or waveform characteristics to tentatively classify other neurone types (Vos et al., Eur. J. Neurosci., 1999; Prsa et al., J. Neurosci., 2009), in some cases supported by juxtacellular labelling (Simpson et al., Prog. Brain Res., 2005; Holtzman et al., J. Physiol., 2006; Barmack and Yakhnitsa, J. Neurosci., 2008; Ruigrok et al., J. Neurosci., 2011), or intracellular staining and / or assessment of membrane properties (Chadderton et al., Nature, 2004; Jorntell and Ekerot, J. Neurosci., 2006; Rancz et al., Nature, 2007). Anaesthetised animals have been widely used as they can provide a ground-truth through neuronal labelling which is much harder to achieve in awake animals where spike-derived measures tend to be relied upon (Lansink et al., Eur. J. Neurosci., 2010). Whilst spike-shapes carry potentially useful information for classifying neuronal classes, they vary with electrode type and the geometric relationship between the electrode and the spike generation zone (Van Dijck et al., Int. J. Neural Syst., 2012). Moreover, spike-shape measurement is achieved with a variety of techniques, making it difficult to compare and standardise between laboratories.In this study we build probabilistic models on the statistics derived from the spike trains of spontaneously active neurones in the cerebellum and the ventral midbrain. The mean spike frequency in combination with the log-interval-entropy (Bhumbra and Dyball, J. Physiol.-London, 2004) of the inter-spike-interval distribution yields the highest prediction accuracy. The cerebellum model consists of two sub-models: a molecular layer - Purkinje layer model and a granular layer - Purkinje layer model. The first model identifies with high accuracy (92.7 %) molecular layer interneurones and Purkinje cells, while the latter identifies with high accuracy (99.2 %) Golgi cells, granule cells, mossy fibers and Purkinje cells. Furthermore, it is shown that the model trained on anaesthetized rat and decerebrate cat data has broad applicability to other species and behavioural states: anaesthetized mice (80 %), awake rabbits (94.2 %) and awake rhesus monkeys (89 - 90 %).Recently, opto-genetics allow to obtain a ground-truth about cell classes. Using opto-genetically identified GABA-ergic and dopaminergic cells we build similar statistical models to identify these neuron types from the ventral midbrain.Hence, this illustrates that our approach will be of general use to a broad variety of laboratories.<br />
<br />
'''Tuesday, 23 Oct 2012''' <br />
* Speaker: Jaimie Sleigh<br />
* Affiliation: University of Auckland<br />
* Host: Fritz/Andrew Szeri<br />
* Status: confirmed<br />
* Title: Is General Anesthesia a failure of cortical information integration<br />
* Abstract: General anesthesia and natural sleep share some commonalities and some differences. Quite a lot is known about the chemical and neuronal effects of general anesthetic drugs. There are two main groups of anesthetic drugs, which can be distinguished by their effects on the EEG. The most commonly used drugs exert a strong GABAergic action; whereas a second group is characterized by minimal GABAergic effects, but significant NMDA blockade. It is less clear which and how these various effects result in failure of the patient to wake up when the surgeon cuts them. I will present some results from experimental brain slice work, and theoretical mean field modelling of anesthesia and sleep, that support the idea that the final common mechanism of both types of anaesthesia is fragmentation of long distance information flow in the cortex.<br />
<br />
'''31 Oct 2012''' (Halloween)<br />
* Speaker: Jonathan Landy<br />
* Affiliation: UCSB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Mean-field replica theory: review of basics and a new approach<br />
* Abstract: Replica theory provides a general method for evaluating the mode of a distribution, and has varied applications to problems in statistical mechanics, signal processing, etc. Evaluation of the formal expressions arising in replica theory represents a formidable technical challenge, but one that physicists have apparently intuited correct methods for handling. In this talk, I will first provide a review of the historical development of replica theory, covering: 1) motivation, 2) the intuited ``Parisi-ansatz" solution, 3) continued controversies, and 4) a survey of applications (including to neural networks). Following this, I will discuss an exploratory effort of mine, aimed at developing an ansatz-free solution method. As an example, I will work out the phase diagram for a simple spin-glass model. This talk is intended primarily as a tutorial.<br />
<br />
'''7 Nov 2012''' <br />
* Speaker: Tom Griffiths<br />
* Affiliation: UC Berkeley<br />
* Host:Daniel Little<br />
* Status: Confirmed<br />
* Title: Identifying human inductive biases<br />
* Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases.<br />
<br />
'''19 Nov 2012''' (Monday) (Thanksgiving week)<br />
* Speaker: Bin Yu<br />
* Affiliation: Dept. of Statistics and EECS, UC Berkeley<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Representation of Natural Images in V4<br />
* Abstract: The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.<br />
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features.<br />
(This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver<br />
and J. Gallant.)<br />
<br />
'''30 Nov 2012''' <br />
* Speaker: Yan Karklin<br />
* Affiliation: NYU<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: <br />
* Abstract: <br />
<br />
'''10 Dec 2012 (note this would be the Monday after NIPS)''' <br />
* Speaker: Marius Pachitariu<br />
* Affiliation: Gatsby / UCL<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: NIPS paper "Learning visual motion in recurrent neural networks"<br />
* Abstract: We present a dynamic nonlinear generative model for visual motion based on a<br />
latent representation of binary-gated Gaussian variables connected in a network. <br />
Trained on sequences of images by an STDP-like rule the model learns <br />
to represent different movement directions in different variables. We use an online <br />
approximate inference scheme that can be mapped to the dynamics of networks <br />
of neurons. Probed with drifting grating stimuli and moving bars of light, neurons <br />
in the model show patterns of responses analogous to those of direction-selective <br />
simple cells in primary visual cortex. We show how the computations of the model <br />
are enabled by a specific pattern of learnt asymmetric recurrent connections. <br />
I will also briefly discuss our application of recurrent neural networks as statistical <br />
models of simultaneously recorded spiking neurons. <br />
<br />
'''12 Dec 2012''' <br />
* Speaker: Ian Goodfellow<br />
* Affiliation: U Montreal<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''7 Jan 2013'''<br />
* Speaker: Stuart Hammeroff<br />
* Affiliation: University of Arizona <br />
* Host: Gautam Agarwal<br />
* Status: confirmed<br />
* Title: Quantum cognition and brain microtubules <br />
* Abstract: Cognitive decision processes are generally seen as classical Bayesian probabilities, but better suited to quantum mathematics. For example: 1) Psychological conflict, ambiguity and uncertainty can be viewed as (quantum) superposition of multiple possible judgments and beliefs. 2) Measurement (e.g. answering a question, reaching a decision) reduces possibilities to definite states (‘constructing reality’, ‘collapsing the wave function’). 3) Previous questions influence subsequent answers, so sequence affects outcomes (‘contextual non-commutativity’). 4) Judgments and choices may deviate from classical logic, suggesting random, or ‘non-computable’ quantum influences. Can quantum cognition operate in the brain? Do classical brain activities simulate quantum processes? Or have biomolecular quantum devices evolved? In this talk I will discuss how a finer scale, intra-neuronal level of quantum information processing in cytoskeletal microtubules can accumulate, operate upon and integrate quantum information and memory for self-collapse to classical states which regulate axonal firings, controlling behavior.<br />
<br />
'''Monday 14 Jan 2013, 1:00pm'''<br />
* Speaker: Dibyendu Mandal <br />
* Affiliation: Physics Dept., University of Maryland (Jarzynski group)<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: An exactly solvable model of Maxwell’s demon<br />
* Abstract: The paradox of Maxwell’s demon has stimulated numerous thought experiments, leading to discussions about the thermodynamic implications of information processing. However, the field has lacked a tangible example or model of an autonomous, mechanical system that reproduces the actions of the demon. To address this issue, we introduce an explicit model of a device that can deliver work to lift a mass against gravity by rectifying thermal fluctuations, while writing information to a memory register. We solve for the steady-state behavior of the model and construct its nonequilibrium phase diagram. In addition to the engine-like action described above, we identify a Landauer eraser region in the phase diagram where the model uses externally supplied work to remove information from the memory register. Our model offers a simple paradigm for investigating the thermodynamics of information processing by exposing a transparent mechanism of operation.<br />
<br />
'''23 Jan 2013'''<br />
* Speaker: Carlos Brody<br />
* Affiliation: Princeton<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Neural substrates of decision-making in the rat<br />
* Abstract: Gradual accumulation of evidence is thought to be a fundamental component of decision-making. Over the last 16 years, research in non-human primates has revealed neural correlates of evidence accumulation in parietal and frontal cortices, and other brain areas . However, the circuit mechanisms underlying these neural correlates remains unknown. Reasoning that a rodent model of evidence accumulation would allow a greater number of experimental subjects, and therefore experiments, as well as facilitate the use of molecular tools, we developed a rat accumulation of evidence task, the "Poisson Clicks" task. In this task, sensory evidence is delivered in pulses whose precisely-controlled timing varies widely within and across trials. The resulting data are analyzed with models of evidence accumulation that use the richly detailed information of each trial’s pulse timing to distinguish between different decision mechanisms. The method provides great statistical power, allowing us to: (1) provide compelling evidence that rats are indeed capable of gradually accumulating evidence for decision-making; (2) accurately estimate multiple parameters of the decision-making process from behavioral data; and (3) measure, for the first time, the diffusion constant of the evidence accumulator, which we show to be optimal (i.e., equal to zero). In addition, the method provides a trial-by-trial, moment-by-moment estimate of the value of the accumulator, which can then be compared in awake behaving electrophysiology experiments to trial-by-trial, moment-by-moment neural firing rate measures. Based on such a comparison, we describe data and a novel analysis approach that reveals differences between parietal and frontal cortices in the neural encoding of accumulating evidence. Finally, using semi-automated training methods to produce tens of rats trained in the Poisson Clicks accumulation of evidence task, we have also used pharmacological inactivation to ask, for the first time, whether parietal and frontal cortices are required for accumulation of evidence, and we are using optogenetic methods to rapidly and transiently inactivate brain regions so as to establish precisely when, during each decision-making trial, it is that each brain region's activity is necessary for performance of the task.<br />
<br />
'''28 Jan 2013'''<br />
* Speaker: Eugene M. Izhikevich<br />
* Affiliation: Brain Corporation<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Spikes<br />
* Abstract: Most communication in the brain is via spikes. While we understand the spike-generation mechanism of individual neurons, we fail to appreciate the spike-timing code and its role in neural computations. The speaker starts with simple models of neuronal spiking and bursting, describes small neuronal circuits that learn spike-timing code via spike-timing dependent plasticity (STDP), and finishes with biologically detailed and anatomically accurate large-scale brain models.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Goren Gordon<br />
* Affiliation: Weizman Intitute<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: Hierarchical Curiosity Loops – Model, Behavior and Robotics<br />
* Abstract: Autonomously learning about one's own body and its interaction with the environment is a formidable challenge, yet it is ubiquitous in biology: every animal’s pup and every human infant accomplish this task in their first few months of life. Furthermore, biological agents’ curiosity actively drives them to explore and experiment in order to expedite their learning progress. To bridge the gap between biological and artificial agents, a formal mathematical theory of curiosity was developed that attempts to explain observed biological behaviors and enable curiosity emergence in robots. In the talk, I will present the hierarchical curiosity loops model, its application to rodent’s exploratory behavior and its implementation in a fully autonomously learning and behaving reaching robot.<br />
<br />
'''29 Jan 2013'''<br />
* Speaker: Jenny Read<br />
* Affiliation: Institute of Neuroscience, Newcastle University<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: Stereoscopic vision<br />
* Abstract: [To be written]<br />
<br />
'''7 Feb 2013'''<br />
* Speaker: Valero Laparra<br />
* Affiliation: University of Valencia<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Empirical statistical analysis of phases in Gabor filtered natural images<br />
* Abstract:<br />
<br />
'''20 Feb 2013'''<br />
* Speaker: Dolores Bozovic<br />
* Affiliation: UCLA<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: Bifurcations and phase-locking dynamics in the auditory system<br />
* Abstract: The inner ear constitutes a remarkable biological sensor that exhibits nanometer-scale sensitivity of mechanical detection. The first step in auditory processing is performed by hair cells, which convert movement into electrical signals via opening of mechanically gated ion channels. These cells are operant in a viscous medium, but can nevertheless sustain oscillations, amplify incoming signals, and even exhibit spontaneous motility, indicating the presence of an underlying active amplification system. Theoretical models have proposed that a hair cell constitutes a nonlinear system with an internal feedback mechanism that can drive it across a bifurcation and into an unstable regime. Our experiments explore the nonlinear response as well as feedback mechanisms that enable self-tuning already at the peripheral level, as measured in vitro on sensory tissue. A simple dynamic systems framework will be discussed, that captures the main features of the experimentally observed behavior in the form of an Arnold Tongue.<br />
<br />
'''27 March 2013'''<br />
* Speaker: Dale Purves<br />
* Affiliation: Duke<br />
* Host: Sarah<br />
* Status: confirmed<br />
* Title: How Visual Evolution Determines What We See<br />
* Abstract: Information about the physical world is excluded from visual stimuli by the nature of biological vision (the inverse optics problem). Nonetheless, humans and other visual animals routinely succeed in their environments. The talk will explain how the assignment of perceptual values to visual stimuli according to the frequency of occurrence of stimulus patterns resolves the inverse problem and determines the basic visual qualities we see. This interpretation of vision implies that the best (and perhaps the only) way to understand visual system circuitry is to evolve it, an idea supported by recent work.<br />
<br />
'''9 April 2013'''<br />
* Speaker: Mounya Elhilali<br />
* Affiliation: Johns Hopkins<br />
* Host: Tyler<br />
* Status: confirmed<br />
* Title: Attention at the cocktail party: Neural bases and computational strategies for auditory scene analysis<br />
* Abstract: The perceptual organization of sounds in the environment into coherent objects is a feat constantly facing the auditory system. It manifests itself in the everyday challenge faced by humans and animals alike to parse complex acoustic information arising from multiple sound sources into separate auditory streams. While seemingly effortless, uncovering the neural mechanisms and computational principles underlying this remarkable ability remain a challenge for both the experimental and theoretical neuroscience communities. In this talk, I discuss the potential role of neuronal tuning in mammalian primary auditory cortex in mediating this process. I also examine the role of mechanisms of attention in adapting this neural representation to reflect both the sensory content and the changing behavioral context of complex acoustic scenes.<br />
<br />
'''17th of April 2013'''<br />
* Speaker: Wiktor Młynarski<br />
* Affiliation: Max Planck Institute for Mathematics in the Sciences<br />
* Host: Urs<br />
* Status: confirmed<br />
* Title: Statistical Models of Binaural Sounds<br />
* Abstract: The auditory system exploits disparities in the sounds arriving at the left and right ear to extract information about the spatial configuration of sound sources. According to the widely acknowledged Duplex Theory, sounds of low frequency are localized based on Interaural Time Differences (ITDs) and localization of high frequency sources relies on Interaural Level Differences (ILDs). Natural sounds, however, possess a rich structure and contain multiple frequency components. This leads to the question: what are the contributions of different cues to sound position identification in the natural environment and how much information do they carry about its spatial structure? In this talk, I will present my attempts to answer the above questions using statistical, generative models of naturalistic (simulated) and fully natural binaural sounds.<br />
<br />
'''15 May 2013'''<br />
* Speaker: Byron Yu<br />
* Affiliation: CMU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
'''22 May 2013'''<br />
* Speaker: Bijan Pesaran<br />
* Affiliation: NYU<br />
* Host: Bruno/Jose (jointly sponsored with CNEP)<br />
* Status: confirmed <br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
=== 2011/12 academic year ===<br />
<br />
'''15 Sep 2011 (Thursday, at noon)'''<br />
* Speaker: Kathrin Berkner<br />
* Affiliation: Ricoh Innovations Inc.<br />
* Host: Ivana Tosic<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''21 Sep 2011'''<br />
* Speaker: Mike Kilgard<br />
* Affiliation: UT Dallas<br />
* Host: Michael Silver<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''27 Sep 2011'''<br />
* Speaker: Moshe Gur<br />
* Affiliation: Dept. of Biomedical Engineering, Technion, Israel Institute of Technology<br />
* Host: Bruno/Stan<br />
* Status: Confirmed<br />
* Title: On the unity of perception: How does the brain integrate activity evoked at different cortical loci?<br />
* Abstract: Any physical device we know, including computers, when comparing A to B must send the information to point C. I have done experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex are stimulated and I argue that the "machine" converging hypothesis cannot explain the perceptual results. Thus we must assume a non-converging mechanism whereby the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target. Once we allow for such a mechanism, many phenomena can be viewed differently. Take for example the question of how and where does multi-sensory integration take place; we perceive a synchronized talking face yet detailed visual and auditory information are represented at very different brain loci.<br />
<br />
'''5 Oct 2011'''<br />
* Speaker: Susanne Still<br />
* Affiliation: University of Hawaii at Manoa<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Predictive power, memory and dissipation in learning systems operating far from thermodynamic equilibrium<br />
* Abstract: Understanding the physical processes that underly the functioning of biological computing machinery often requires describing processes that occur far from thermodynamic equilibrium. In recent years significant progress has been made in this area, most notably Jarzynski’s work relation and Crooks’ fluctuation theorem. In this talk I will explore how dissipation of energy is related to a system's information processing inefficiency. The focus is on driven systems that are embedded in a stochastic operating environment. If we describe the system as a state machine, then we can interpret the stochastic dynamics as performing a computation that results in an (implicit) model of the stochastic driving signal. I will show that instantaneous non-predictive information, which serves as a measure of model inefficiency, provides a lower bound on the average dissipated work. This implies that learning systems with larger predictive power can operate more energetically efficiently. We could speculate that perhaps biological systems may have evolved to reflect this kind of adaptation. One interesting insight here is that purely physical notions require what is perfectly in line with the general belief that a useful model must be predictive (at fixed model complexity). Our result thereby ties together ideas from learning theory with basic non-equilibrium thermodynamics.<br />
<br />
'''19 Oct 2011'''<br />
* Speaker: Graham Cummins<br />
* Affiliation: WSU<br />
* Host: Jeff Teeters<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''26 Oct 2011'''<br />
* Speaker: Shinji Nishimoto<br />
* Affiliation: Gallant lab, UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''14 Dec 2011'''<br />
* Speaker: Austin Roorda<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: How the unstable eye sees a stable and moving world<br />
* Abstract:<br />
<br />
'''11 Jan 2012'''<br />
* Speaker: Ken Nakayama<br />
* Affiliation: Harvard University<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Subjective Contours<br />
* Abstract: The concept of the receptive field in visual science has been transformative. It fueled great discoveries of the second half of the 20th C, providing the dominant understanding of how the visual system works at its early stages. Its reign has been extended to the field of object recognition where in the form of a linear classifier, it provides a framework to understand visual object recognition (DiCarlo and Cox, 2007).<br />
Untamed, however, are areas of visual perception, now more or less ignored, dubbed variously as the 2.5 D sketch, mid-level vision, surface representations. Here, neurons with their receptive fields seem unable to bridge the gap, to supply us with even a plausible speculative framework to understand amodal completion, subjective contours and other surface phenomena. Correspondingly, these areas have become backwater, ignored, leapt over.<br />
Subjective contours, however, remain as vivid as ever, even more so.<br />
Everyday, our visual system makes countless visual inferences as to the layout of the world surfaces and objects. What’s remarkable is that subjective contours visibly reveal these inferences.<br />
<br />
'''Tuesday, 24 Jan 2012'''<br />
* Speaker: Aniruddha Das<br />
* Affiliation: Columbia University<br />
* Host: Fritz<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''22 Feb 2012'''<br />
* Speaker: Elad Schneidman <br />
* Affiliation: Department of Neurobiology, Weizmann Institute of Science<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Sparse high order interaction networks underlie learnable neural population codes<br />
* Abstract:<br />
<br />
'''29 Feb 2012 (at noon as usual)'''<br />
* Speaker: Heather Read<br />
* Affiliation: U. Connecticut<br />
* Host: Mike DeWeese<br />
* Status: confirmed<br />
* Title: "Transformation of sparse temporal coding from auditory colliculus and cortex"<br />
* Abstract: TBD<br />
<br />
'''1 Mar 2012 (note: Thurs)'''<br />
* Speaker: Daniel Zoran<br />
* Affiliation: Hebrew University, Jerusalem<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 Mar 2012'''<br />
* Speaker: David Sivak<br />
* Affiliation: UCB<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''8 Mar 2012'''<br />
* Speaker: Ivan Schwab<br />
* Affiliation: UC Davis<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Evolution's Witness: How Eyes Evolved<br />
* Abstract:<br />
<br />
'''14 Mar 2012'''<br />
* Speaker: David Sussillo<br />
* Affiliation:<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 April 2012'''<br />
* Speaker: Kristofer Bouchard<br />
* Affiliation: UCSF<br />
* Host: Bruno<br />
* Status: confirmed<br />
* Title: Cortical Foundations of Human Speech Production<br />
* Abstract:<br />
<br />
'''23 May 2012''' (rescheduled from April 11)<br />
* Speaker: Logan Grosenick<br />
* Affiliation: Stanford, Deisseroth & Suppes Labs<br />
* Host: Jascha<br />
* Status: confirmed<br />
* Title: Acquisition, creation, & analysis of 4D light fields with applications to calcium imaging & optogenetics<br />
* Abstract: In Light Field Microscopy (LFM), images can be computationally refocused after they are captured [1]. This permits acquiring focal stacks and reconstructing volumes from a single camera frame. In Light Field Illumination (LFI), the same ideas can be used to create an illumination system that can deliver focused light to any position in a volume without moving optics, and these two devices (LFM/LFI) can be used together in the same system [2]. So far, these imaging and illumination systems have largely been used independently in proof-of-concept experiments [1,2]. In this talk I will discuss applications of a combined scanless volumetric imaging and volumetric illumination system applied to 4D calcium imaging and photostimulation of neurons in vivo and in vitro. The volumes resulting from these methods are large (>500,000 voxels per time point), collected at 10-100 frames per second, and highly correlated in space and time. Analyzing such data has required the development and application of machine learning methods appropriate to large, sparse, nonnegative data, as well as the estimation of neural graphical models from calcium transients. This talk will cover the reconstruction and creation of volumes in a microscope using Light Fields [1,2], and the current state-of-the-art for analyzing these large volumes in the context of calcium imaging and optogenetics. <br />
<br />
[1] M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz. Light Field Microscopy. ACM Transactions on Graphics 25(3), Proceedings of SIGGRAPH 2006.<br />
[2] M. Levoy, Z. Zhang, and I. McDowall. Recording and controlling the 4D light field in a microscope. Journal of Microscopy, Volume 235, Part 2, 2009, pp. 144-162. Cover article.<br />
<br />
BIO: Logan received bachelors degrees with honors in Biology and Psychology from Stanford, and a Masters in Statistics from Stanford. He is a Ph.D. candidate in the Neurosciences Program working in the labs of Karl Deisseroth and Patrick Suppes, and a trainee at the Stanford Center for Mind, Brain, and Computation. He is interested in developing and applying novel computational imaging and machine learning techniques in order to observe, control, and understand neuronal circuit dynamics.<br />
<br />
'''7 June 2012''' (Thursday)<br />
* Speaker: Mitya Chklovskii<br />
* Affiliation: janelia<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract<br />
<br />
'''27 June 2012''' <br />
* Speaker: Jerry Feldman<br />
* Affiliation:<br />
* Host: Bruno<br />
* Status:<br />
* Title:<br />
* Abstract:<br />
<br />
'''30 July 2012''' <br />
* Speaker: Lucas Theis<br />
* Affiliation: Matthias Bethge lab, Werner Reichardt Centre for Integrative Neuroscience, Tübingen<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Hierarchical models of natural images<br />
* Abstract: Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.<br />
<br />
(joint work with Reshad Hosseini and Matthias Bethge)<br />
<br />
=== 2010/11 academic year ===<br />
<br />
'''02 Sep 2010'''<br />
* Speaker: Johannes Burge<br />
* Affiliation: University of Texas at Austin<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 Sep 2010'''<br />
* Speaker: Tobi Szuts<br />
* Affiliation: Meister Lab/ Harvard U.<br />
* Host: Mike DeWeese<br />
* Status: Confirmed<br />
* Title: Wireless recording of neural activity in the visual cortex of a freely moving rat.<br />
* Abstract: Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to the recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from a freely moving animal the size of a rat or larger. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them onto a single output line, and transmits that output by radio frequency to a receiver and recording computer up to >60 m away. The system introduces less than 4 ?V RMS of electrode-referred noise, comparable to wired recording systems and considerably less than biological noise. The system has greater channel count or transmission distance than existing telemetry systems. The wireless system has been used to record from the visual cortex of a rat during unconstrained conditions. Outdoor recordings show V1 activity is modulated by nest-building activity. During unguided behavior indoors, neurons responded rapidly and consistently to changes in light level, suppressive effects were prominent in response to an illuminant transition, and firing rate was strongly modulated by locomotion. Neural firing in the visual cortex is relatively sparse and moderate correlations are observed over large distances, suggesting that synchrony is driven by global processes.<br />
<br />
'''29 Sep 2010'''<br />
* Speaker: Vikash Gilja<br />
* Affiliation: Stanford University<br />
* Host: Charles<br />
* Status: Confirmed<br />
* Title: Towards Clinically Viable Neural Prosthetic Systems.<br />
* Abstract:<br />
<br />
'''20 Oct 2010'''<br />
* Speaker: Alexandre Francois<br />
* Affiliation: USC<br />
* Host: <br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 Nov 2010'''<br />
* Speaker: Eric Jonas and Vikash Mansinghka<br />
* Affiliation: Navia Systems<br />
* Host: Jascha<br />
* Status: Confirmed<br />
* Title: Natively Probabilistic Computation: Principles, Artifacts, Architectures and Applications<br />
* Abstract: Complex probabilistic models and Bayesian inference are becoming<br />
increasingly critical across science and industry, especially in<br />
large-scale data analysis. They are also central to our best<br />
computational accounts of human cognition, perception and action.<br />
However, all these efforts struggle with the infamous curse of<br />
dimensionality. Rich probabilistic models can seem hard to write and<br />
even harder to solve, as specifying and calculating probabilities<br />
often appears to require the manipulation of exponentially (and<br />
sometimes infinitely) large tables of numbers.<br />
<br />
We argue that these difficulties reflect a basic mismatch between the<br />
needs of probabilistic reasoning and the deterministic, functional<br />
orientation of our current hardware, programming languages and CS<br />
theory. To mitigate these issues, we have been developing a stack of<br />
abstractions for natively probabilistic computation, based around<br />
stochastic simulators (or samplers) for distributions, rather than<br />
evaluators for deterministic functions. Ultimately, our aim is to<br />
produce a model of computation and the associated hardware and<br />
programming tools that are as suited for uncertain inference and<br />
decision-making as our current computers are for precise arithmetic.<br />
<br />
In this talk, we will give an overview of the entire stack of<br />
abstractions supporting natively probabilistic computation, with<br />
technical detail on several hardware and software artifacts we have<br />
implemented so far. we will also touch on some new theoretical results<br />
regarding the computational complexity of probabilistic programs.<br />
Throughout, we will motivate and connect this work to some current<br />
applications in biomedical data analysis and computer vision, as well<br />
as potential hypotheses regarding the implementation of probabilistic<br />
computation in the brain.<br />
<br />
This talk includes joint work with Keith Bonawitz, Beau Cronin,<br />
Cameron Freer, Daniel Roy and Joshua Tenenbaum.<br />
<br />
BRIEF BIOGRAPHY<br />
<br />
Vikash Mansinghka is a co-founder and the CTO of Navia Systems, a<br />
venture-funded startup company building natively probabilistic<br />
computing machines. He spent 10 years at MIT, eventually earning an<br />
SB. in Mathematics, an SB. in Computer Science, an MEng in Computer<br />
Science, and a PhD in Computation. He held graduate fellowships from<br />
the NSF and MIT's Lincoln Laboratories, and his PhD dissertation won<br />
the 2009 MIT George M. Sprowls award for best dissertation in computer<br />
science. He currently serves on DARPA's Information Science and<br />
Technology (ISAT) Study Group.<br />
<br />
Eric Jonas is a co-founder of Navia Systems, responsible for in-house<br />
accelerated inference research and development. He spent ten years at<br />
MIT, where he earned SB degrees in electrical engineering and computer<br />
science and neurobiology, an MEng in EECS, with a neurobiology PhD<br />
expected really soon. He’s passionate about biological applications<br />
of probabilistic reasoning and hopes to use Navia’s capabilities to<br />
combine data from biological science, clinical histories, and patient<br />
outcomes into seamless models.<br />
<br />
'''8 Nov 2010'''<br />
* Speaker: Patrick Ruther<br />
* Affiliation: Imtek, University of Freiburg<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract: TBD<br />
<br />
'''10 Nov 2010'''<br />
* Speaker: Aurel Lazar<br />
* Affiliation: Department of Electrical Engineering, Columbia University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Encoding Visual Stimuli with a Population of Hodgkin-Huxley Neurons<br />
* Abstract: We first present a general framework for the reconstruction of natural video<br />
scenes encoded with a population of spiking neural circuits with random thresholds.<br />
The visual encoding system consists of a bank of filters, modeling the visual<br />
receptive fields, in cascade with a population of neural circuits, modeling encoding<br />
with spikes in the early visual system.<br />
The neuron models considered include integrate-and-fire neurons and ON-OFF<br />
neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed<br />
to be random. We show that for both time-varying and space-time-varying stimuli neural<br />
spike encoding is akin to taking noisy measurements on the stimulus.<br />
Second, we formulate the reconstruction problem as the minimization of a<br />
suitable cost functional in a finite-dimensional vector space and provide an explicit<br />
algorithm for stimulus recovery. We also present a general solution using the theory of<br />
smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both<br />
synthetic video as well as for natural scenes and show that the quality of the<br />
reconstruction degrades gracefully as the threshold variability of the neurons increases.<br />
Third, we demonstrate a number of simple operations on the original visual stimulus<br />
including translations, rotations and zooming. All these operations are natively executed<br />
in the spike domain. The processed spike trains are decoded for the faithful recovery<br />
of the stimulus and its transformations.<br />
Finally, we extend the above results to neural encoding circuits built with Hodking-Huxley<br />
neurons.<br />
References:<br />
Aurel A. Lazar, Eftychios A. Pnevmatikakis and Yiyin Zhou,<br />
Encoding Natural Scenes with Neural Circuits with Random Thresholds, Vision Research, 2010,<br />
Special Issue on Mathematical Models of Visual Coding,<br />
http://dx.doi.org/10.1016/j.visres.2010.03.015<br />
Aurel A. Lazar,<br />
Population Encoding with Hodgkin-Huxley Neurons,<br />
IEEE Transactions on Information Theory, Volume 56, Number 2, pp. 821-837, February, 2010,<br />
Special Issue on Molecular Biology and Neuroscience,<br />
http://dx.doi.org/10.1109/TIT.2009.2037040<br />
<br />
'''11 Nov 2010''' (UCB holiday)<br />
* Speaker: Martha Nari Havenith<br />
* Affiliation: UCL<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Finding spike timing in the visual cortex - Oscillations as the internal clock of vision?<br />
* Abstract:<br />
<br />
'''19 Nov 2010''' (note: on Friday because of SFN)<br />
* Speaker: Dan Butts<br />
* Affiliation: UMD<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: Common roles of inhibition in visual and auditory processing.<br />
* Abstract: The role of inhibition in sensory processing is often obscured in extracellular recordings, because the absence of a neuronal response associated with inhibition might also be explained by a simple lack of excitation. However, increasingly, evidence from intracellular recordings demonstrates important roles of inhibition in shaping the stimulus selectivity of sensory neurons in both the visual and auditory systems. We have developed a nonlinear modeling approach that can identify putative excitatory and inhibitory inputs to a neuron using standard extracellular recordings, and have applied these techniques to understand the role of inhibition in shaping sensory processing in visual and auditory areas. In pre-cortical visual areas (retina and LGN), we find that inhibition likely plays a role in generating temporally precise responses, and mediates adaptation to changing contrast. In an auditory pre-cortical area (inferior colliculus) identified inhibition has nearly identical appearance and functions in temporal processing and adaptation. Thus, we predict common roles of inhibition in these sensory areas, and more generally demonstrate general methods for characterizing the nonlinear computations that comprise sensory processing.<br />
<br />
'''24 Nov 2010'''<br />
* Speaker: Eizaburo Doi<br />
* Affiliation: NYU<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''29 Nov 2010 - informal talk'''<br />
* Speaker: Eero Lehtonen<br />
* Affiliation: UTU Finland<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Memristors<br />
* Abstract:<br />
<br />
'''1 Dec 2010'''<br />
* Speaker: Gadi Geiger<br />
* Affiliation: MIT<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: Visual and Auditory Perceptual Modes that Characterize Dyslexics<br />
* Abstract: I will describe how dyslexics’ visual and auditory perception is wider and more diffuse than that of typical readers. This suggests wider neural tuning in dyslexics. In addition I will describe how this processing relates to difficulties in reading. Strengthening the argument and more so helping dyslexics I will describe a regimen of practice that results in improved reading in dyslexics while narrowing perception.<br />
<br />
<br />
'''13 Dec 2010'''<br />
* Speaker: Jorg Lueke<br />
* Affiliation: FIAS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Linear and Non-linear Approaches to Component Extraction and Their Applications to Visual Data<br />
* Abstract: In the nervous system of humans and animals, sensory data are represented as combinations of elementary data components. While for data such as sound waveforms the elementary components combine linearly, other data can better be modeled by non-linear forms of component superpositions. I motivate and discuss two models with binary latent variables: one using standard linear superpositions of basis functions and one using non-linear superpositions. Crucial for the applicability of both models are efficient learning procedures. I briefly introduce a novel training scheme (ET) and show how it can be applied to probabilistic generative models. For linear and non-linear models the scheme efficiently infers the basis functions as well as the level of sparseness and data noise. In large-scale applications to image patches, we show results on the statistics of inferred model parameters. Differences between the linear and non-linear models are discussed, and both models are compared to results of standard approaches in the literature and to experimental findings. Finally, I briefly discuss learning in a recent model that takes explicit component occlusions into account.<br />
<br />
'''15 Dec 2010'''<br />
* Speaker: Claudia Clopath<br />
* Affiliation: Universite Paris Decartes<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
<br />
'''18 Jan 2011'''<br />
* Speaker: Siwei Lyu<br />
* Affiliation: Computer Science Department, University at Albany, SUNY<br />
* Host: Bruno<br />
* Status: confirmed <br />
* Title: Divisive Normalization as an Efficient Coding Transform: Justification and Evaluation<br />
* Abstract:<br />
<br />
'''19 Jan 2011'''<br />
* Speaker: David Field (informal talk)<br />
* Affiliation: <br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''25 Jan 2011'''<br />
* Speaker: Ruth Rosenholtz<br />
* Affiliation: Dept. of Brain & Cognitive Sciences, Computer Science and AI Lab, MIT<br />
* Host: Bruno<br />
* Status: Confirmed <br />
* Title: What your visual system sees where you are not looking<br />
* Abstract:<br />
<br />
'''26 Jan 2011'''<br />
* Speaker: Ernst Niebur<br />
* Affiliation: Johns Hopkins U<br />
* Host: Fritz<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''16 March 2011'''<br />
* Speaker: Vladimir Itskov<br />
* Affiliation: University of Nebraska-Lincoln<br />
* Host: Chris<br />
* Status: Confirmed <br />
* Title: <br />
* Abstract:<br />
<br />
'''23 March 2011'''<br />
* Speaker: Bruce Cumming<br />
* Affiliation: National Institutes of Health<br />
* Host: Ivana<br />
* Status: Confirmed<br />
* Title: TBD<br />
* Abstract:<br />
<br />
'''27 April 2011'''<br />
* Speaker: Lubomir Bourdev<br />
* Affiliation: Computer Science, UC Berkeley<br />
* Host:Bruno<br />
* Status: Confirmed<br />
* Title: "Poselets and Their Applications in High-Level Computer Vision Problems"<br />
* Abstract:<br />
<br />
'''12 May 2011 (note: Thursday)'''<br />
* Speaker: Jack Culpepper<br />
* Affiliation: Redwood Center/EECS<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''26 May 2011'''<br />
* Speaker: Ian Stevenson<br />
* Affiliation: Northwestern University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Explaining tuning curves by estimating interactions between neurons<br />
* Abstract: One of the central tenets of systems neuroscience is that tuning curves are a byproduct of the interactions between neurons. Using multi-electrode recordings and recently developed inference techniques we can begin to examine this idea in detail and study how well we can explain the functional properties of neurons using the activity of other simultaneously recorded neurons. Here we examine datasets from 6 different brain areas recorded during typical sensorimotor tasks each with ~100 simultaneously recorded neurons. Using these datasets we measured the extent to which interactions between neurons can explain the tuning properties of individual neurons. We found that, in almost all areas, modeling interactions between 30-50 neurons allows more accurate spike prediction than tuning curves. This suggests that tuning can, in some sense, be explained by interactions between neurons in a variety of brain areas, even when recordings consist of relatively small numbers of neurons.<br />
<br />
'''1 June 2011'''<br />
* Speaker: Michael Oliver<br />
* Affiliation: Gallant lab<br />
* Host: Bruno<br />
* Status: Tentative <br />
* Title: <br />
* Abstract:<br />
<br />
'''8 June 2011'''<br />
* Speaker: Alyson Fletcher<br />
* Affiliation: UC Berkeley<br />
* Host: Bruno<br />
* Status: tentative<br />
* Title: Generalized Approximate Message Passing for Neural Receptive Field Estimation and Connectivity<br />
* Abstract: Fundamental to understanding sensory encoding and connectivity of neurons are effective tools for developing and validating complex mathematical models from experimental data. In this talk, I present a graphical models approach to the problems of neural connectivity reconstruction under multi-neuron excitation and to receptive field estimation of sensory neurons in response to stimuli. I describe a new class of Generalized Approximate Message Passing (GAMP) algorithms for a general class of inference problems on graphical models based Gaussian approximations of loopy belief propagation. The GAMP framework is extremely general, provides a systematic procedure for incorporating a rich class of nonlinearities, and is computationally tractable with large amounts of data. In addition, for both the connectivity reconstruction and parameter estimation problems, I show that GAMP-based estimation can naturally incorporate sparsity constraints in the model that arise from the fact that only a small fraction of the potential inputs have any influence on the output of a particular neuron. A simulation of reconstruction of cortical neural mapping under multi-neuron excitation shows that GAMP offers improvement over previous compressed sensing methods. The GAMP method is also validated on estimation of linear nonlinear Poisson (LNP) cascade models for neural responses of salamander retinal ganglion cells.<br />
<br />
=== 2009/10 academic year ===<br />
<br />
'''2 September 2009''' <br />
* Speaker: Keith Godfrey<br />
* Affiliation: University of Cambridge<br />
* Host: Tim<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract:<br />
<br />
'''7 October 2009'''<br />
* Speaker: Anita Schmid<br />
* Affiliation: Cornell University<br />
* Host: Kilian<br />
* Status: Confirmed<br />
* Title: Subpopulations of neurons in visual area V2 perform differentiation and integration operations in space and time<br />
* Abstract: The interconnected areas of the visual system work together to find object boundaries in visual scenes. Primary visual cortex (V1) mainly extracts oriented luminance boundaries, while secondary visual cortex (V2) also detects boundaries defined by differences in texture. How the outputs of V1 neurons are combined to allow for the extraction of these more complex boundaries in V2 is as of yet unclear. To address this question, we probed the processing of orientation signals in single neurons in V1 and V2, focusing on response dynamics of neurons to patches of oriented gratings and to combinations of gratings in neighboring patches and sequential time frames. We found two kinds of response dynamics in V2, both of which are different from those of V1 neurons. While V1 neurons in general prefer one orientation, one subpopulation of V2 neurons (“transient”) shows a temporally dynamic preference, resulting in a preference for changes in orientation. The second subpopulation of V2 neurons (“sustained”) responds similarly to V1 neurons, but with a delay. The dynamics of nonlinear responses to combinations of gratings reinforce these distinctions: the dynamics enhance the preference of V1 neurons for continuous orientations, and enhance the preference of V2 transient neurons for discontinuous ones. We propose that transient neurons in V2 perform a differentiation operation on the V1 input, both spatially and temporally, while the sustained neurons perform an integration operation. We show that a simple feedforward network with delayed inhibition can account for the temporal but not for the spatial differentiation operation.<br />
<br />
'''28 October 2009'''<br />
* Speaker: Andrea Benucci<br />
* Affiliation: Institute of Ophthalmology, University College London<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Stimulus dependence of the functional connectivity between neurons in primary visual cortex<br />
* Abstract: It is known that visual stimuli are encoded by the concerted activity of large populations of neurons in visual cortical areas. However, it is only recently that recording techniques have been made available to study such activations from large ensembles of neurons simultaneously, with millisecond temporal precision and tens of microns spatial resolution. I will present data from voltage-sensitive dye (VSD) imaging and multi-electrode recordings (“Utah” probes) from the primary visual cortex of the cat (V1). I will discuss the relationship between two fundamental cortical maps of the visual system: the map of retinotopy and the map of orientation. Using spatially localized and full-field oriented stimuli, we studied the functional interdependency of these maps. I will describe traveling and standing waves of cortical activity and their key role as a dynamical substrate for the spatio-temporal coding of visual information. I will further discuss the properties of the spatio-temporal code in the context of continuous visual stimulation. While recording population responses to a sequence of oriented stimuli, we asked how responses to individual stimuli summate over time. We found that such rules are mostly linear, supporting the idea that spatial and temporal codes in area V1 operate largely independently. However, these linear rules of summation fail when the visual drive is removed, suggesting that the visual cortex can readily switch between a dynamical regime where either feed-forward or intra-cortical inputs determine the response properties of the network.<br />
<br />
'''12 November 2009 (Thursday)'''<br />
* Speaker: Song-Chun Zhu<br />
* Affiliation: UCLA<br />
* Host: Jimmy<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''18 November 2009'''<br />
* Speaker: Dan Graham<br />
* Affiliation: Dept. of Mathematics, Dartmouth College<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: The Packet-Switching Brain: A Hypothesis<br />
* Abstract: Despite great advances in our understanding of neural responses to natural stimuli, the basic structure of the neural code remains elusive. In this talk, I will describe a novel hypothesis regarding the fundamental structure of neural coding in mammals. In particular, I propose that an internet-like routing architecture (specifically packet-switching) underlies neocortical processing, and I propose means of testing this hypothesis via neural response sparseness measurements. I will synthesize a host of suggestive evidence that supports this notion and will, more generally, argue in favor of a large scale shift from the now dominant “computer metaphor,” to the “internet metaphor.” This shift is intended to spur new thinking with regard to neural coding, and its main contribution is to privilege communication over computation as the prime goal of neural systems.<br />
<br />
'''16 December 2009'''<br />
* Speaker: Pietro Berkes<br />
* Affiliation: Volen Center for Complex Systems, Brandeis University<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Generative models of vision: from sparse coding toward structured models<br />
* Abstract: From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.<br />
<br />
'''6 January 2010'''<br />
* Speaker: Susanne Still<br />
* Affiliation: U of Hawaii<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''20 January 2010'''<br />
* Speaker: Tom Dean<br />
* Affiliation: Google<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: Accelerating Computer Vision and Machine Learning Algorithms with Graphics Processors<br />
* Abstract: Graphics processors (GPUs) and massively-multi-core architectures are becoming more powerful, less costly and more energy efficient, and the related programming language issues are beginning to sort themselves out. That said most researchers don’t want to be writing code that depends on any particular architecture or parallel programming model. Linear algebra, Fourier analysis and image processing have standard libraries that are being ported to exploit SIMD parallelism in GPUs. We can depend on the massively-multiple-core machines du jour to support these libraries and on the high-performance-computing (HPC) community to do the porting for us or with us. These libraries can significantly accelerate important applications in image processing, data analysis and information retrieval. We can develop APIs and the necessary run-time support so that code relying on these libraries will run on any machine in a cluster of computers but exploit GPUs whenever available. This strategy allows us to move toward hybrid computing models that enable a wider range of opportunities for parallelism without requiring the special training of programmers or the disadvantages of developing code that depends on specialized hardware or programming models. This talk summarizes the state of the art in massively-multi-core architectures, presents experimental results that demonstrate the potential for significant performance gains in the two general areas of image processing and machine learning, provides examples of the proposed programming interface, and some more detailed experimental results on one particular problem involving video-content analysis.<br />
<br />
'''27 January 2010'''<br />
* Speaker: David Philiponna<br />
* Affiliation: Paris<br />
* Host: Bruno<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
''''24 Feburary 2010'''<br />
* Speaker: Gordon Pipa<br />
* Affiliation: U Osnabrueck/MPI Frankfurt<br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''3 March 2010'''<br />
* Speaker: Gaute Einevoll<br />
* Affiliation: UMB, Norway<br />
* Host: Amir<br />
* Status: Confirmed<br />
* Title: TBA<br />
* Abstract: TBA<br />
<br />
<br />
'''4 March 2010'''<br />
* Speaker: Harvey Swadlow<br />
* Affiliation: <br />
* Host: Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''8 April 2010'''<br />
* Speaker: Alan Yuille <br />
* Affiliation: UCLA<br />
* Host: Amir<br />
* Status: Confirmed (for 1pm)<br />
* Title: <br />
* Abstract:<br />
<br />
'''28 April 2010'''<br />
* Speaker: Dharmendra Modha - cancelled<br />
* Affiliation: IBM<br />
* Host:Fritz<br />
* Status: Confirmed<br />
* Title: <br />
* Abstract:<br />
<br />
'''5 May 2010'''<br />
* Speaker: David Zipser<br />
* Affiliation: UCB<br />
* Host: Daniel Little<br />
* Status: Tentative<br />
* Title: Brytes 2:<br />
* Abstract:<br />
<br />
Brytes are little brains that can be assembled into larger, smarter brains. In my first talk I presented a biologically plausible, computationally tractable model of brytes and described how they can be used as subunits to build brains with interesting behaviors.<br />
<br />
In this talk I will first show how large numbers of brytes can cooperate to perform complicated actions such as arm and hand manipulations in the presence of obstacles. Then I describe a strategy for a higher level of control that informs each bryte what role it should play in accomplishing the current task. These results could have considerable significance for understanding the brain and possibly be applicable to robotics and BMI.<br />
<br />
'''12 May 2010'''<br />
* Speaker: Frank Werblin (Redwood group meeting - internal only)<br />
* Affiliation: Berkeley<br />
* Host: Bruno<br />
* Status: Tentative<br />
* Title: <br />
* Abstract:<br />
<br />
'''19 May 2010'''<br />
* Speaker: Anna Judith<br />
* Affiliation: UCB<br />
* Host: Daniel Little (Redwood Lab Meeting - internal only)<br />
* Status: confirmed<br />
* Title: <br />
* Abstract:</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8482TCN2016-02-25T00:18:26Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font><br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8481TCN2016-02-25T00:16:06Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font><br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8480TCN2016-02-25T00:15:43Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font><br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [<br />
https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8479TCN2016-02-25T00:14:53Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font><br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex<br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8469TCN2016-02-10T00:35:26Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font><br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font><br />
<br />
[Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain<br />
<br />
[Feb 10] Cancelled - EECS Colloquium <br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8468TCN2016-02-05T00:13:06Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Eric Dodds</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font><br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font><br />
<br />
[Feb 17] <font color="#ff0000">Mayur Mudigonda</font><br />
<br />
[Feb 10] <font color="#ff0000">Paxon Frady</font><br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8467TCN2016-02-04T21:28:46Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 18]<font color="#ff0000"> Karl Zipser</font><br />
<br />
[May 11] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Eric Dodds</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font><br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font><br />
<br />
[Feb 17] <font color="#ff0000">Mayur Mudigonda</font><br />
<br />
[Feb 10] <font color="#ff0000">Paxon Frady</font><br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8466TCN2016-02-04T21:11:34Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[May 11] <font color="#ff0000">Mr. (Alex) Anderson</font><br />
<br />
[May 04] <font color="#ff0000">Chris Warner</font><br />
<br />
[Apr 27] <font color="#ff0000">Eric Dodds</font><br />
<br />
[Apr 20] <font color="#ff0000">Dylan Paiton</font><br />
<br />
[Apr 13] <font color="#ff0000">Kohta Ishikawa</font><br />
<br />
[Apr 06] <font color="#ff0000">Alejandro Bujan</font><br />
<br />
[Mar 30] <font color="#ff0000">Guy Isley</font><br />
<br />
[Mar 23] <font color="#ff0000">Brian Cheung</font><br />
<br />
[Mar 16] <font color="#ff0000">Yubei Chen</font><br />
<br />
[Mar 09] <font color="#ff0000">Sean Mackesey</font><br />
<br />
[Mar 02] <font color="#ff0000">Charles Garfinkle</font><br />
<br />
[Feb 24] <font color="#ff0000">Eric Weiss</font><br />
<br />
[Feb 17] <font color="#ff0000">Mayur Mudigonda</font><br />
<br />
[Feb 10] <font color="#ff0000">Paxon Frady</font><br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8465TCN2016-02-04T21:00:38Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[Feb 24] <font color="#ff0000"></font><br />
<br />
[Feb 17] <font color="#ff0000">Mayur Mudigonda</font><br />
<br />
[Feb 10] <font color="#ff0000">Paxon Frady</font><br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=Main_Page&diff=8459Main Page2016-02-04T02:24:24Z<p>Dpaiton: /* Internal Organization */</p>
<hr />
<div>The Redwood Center '''homepage''' is [http://redwood.berkeley.edu/ here].<br />
<br />
== News ==<br />
<br />
The [[Redwood Center Inaugural Symposium DVD]] is online!<br />
<br />
== Remarks for new users ==<br />
<br />
# You can use the 'preferences' link at the top of the page to configure this wiki. In particular, you might want to change the 'skin' to ''MonoBookRedwood'' in order to take full advantage of the wiki functionality<br />
# Please see the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.<br />
# The [http://meta.wikimedia.org/wiki/MediaWiki_FAQ FAQ] is also useful.<br />
# You might also want to sign up on our [[mailinglist]]<br />
<br />
Have fun!<br />
--[[User:Kilian|Kilian]] 16:45, 8 November 2005 (PST)<br />
<br />
== Public Website Pages ==<br />
<br />
# Here is the page for the '''[[Mission and Research]]''' of the RedwoodCenter!<br />
# Here is the wiki version of the [[People at the Redwood Center]] page.<br />
# Here is the page for editing [[Publications]]<br />
<br />
== Internal Organization ==<br />
<br />
# [[Lab meeting schedule]] for our Friday lab meetings.<br />
# Here is the [[Seminars]] page for the organization of our weekly seminar.<br />
# Here is the wiki page for [[TCN|Topics in Computational Neuroscience (TCN)]] journal club.<br />
# Here is the wiki page for [[OMJC|Topics in Ocular Motion and Stable Visual Perception (OM)]] journal club.<br />
# Internal [http://www.google.com/calendar/embed?src=5fvgm6d1nfflb7hcvcc5acbt7s%40group.calendar.google.com&ctz=America/Los_Angeles Calendar] used for Conference Room reservation. See [[Calendars]] for information on how to login to add another reservation or event.<br />
# There are also calendars for the overflow rooms Evans [http://goo.gl/G4mO5N 550] and [http://goo.gl/suIbHN 552] if you'd like to reserve them.<br />
# Here are some [[Mentorship Resources|resources on mentorship]]. Consider serving as mentor-- it's an excellent way to give back.<br />
# We are no longer having tea time. Here are the [[Tea Time Notes]] from previous meetings.<br />
<br />
== Redwood Resources ==<br />
<br />
# [[Cluster]] - information on using the cortex cluster<br />
# [[Mailinglist]] - has all of the redwood email lists with descriptions<br />
# Instructions for using the RES [[Purchasing System]].<br />
# Here is a resource for presenting our Redwood identity [[Logos]]<br />
# Here is a page with information how to get an [[RSS feed]] for our web pages ([[Image:RSS.gif|RSS]]).<br />
# [[VS298: Neural Computation]] course, Fall 08.<br />
# Who is in charge of X? [[Responsibilities]]<br />
# [[NON-EQUILIBRIUM CLUB]] [[Essential Reading]]<br />
# [[Brain_Network_Dynamics_2007]]<br />
<br />
== Older pages (possibly in need of update) ==<br />
<br />
# old course page [[VS298 (Fall 06): Neural Computation]].<br />
# Here is the table about [[Graphical Models]] we discussed in the journal club.<br />
# Here is a page to suggest [[Purchase Items]] (i.e. books).<br />
# [[Summer Courses]] related to computational/theoretical neuroscience.<br />
# [[Cosyne 2007 Workshops]]<br />
# Photos from the [[Pt Reyes hike]] (July 29, 2006), and the [[Mt Diablo hike]] (August 25, 2006).<br />
# [[Redwood Center Meeting 2008]]</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8458TCN2016-02-04T01:22:11Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[Feb 24] <font color="#ff0000"></font><br />
<br />
[Feb 17] <font color="#ff0000">Mayur Mudigonda</font><br />
<br />
[Feb 10] <font color="#ff0000"></font><br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
<br />
=== [[Past TCN Papers]] ===<br />
<br />
=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
<br />
=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
<br />
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
<br />
If you have questions, please email the club organizer James Arnemann.<br />
<br />
=== E-mail List ===<br />
<br />
To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
<br />
=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
<br />
===[[Suggestion Board]]===</div>Dpaitonhttps://rctn.org/w/index.php?title=TCN&diff=8447TCN2016-01-25T23:50:44Z<p>Dpaiton: /* Spring 2016 */</p>
<hr />
<div>== Topics in Computational Neuroscience ==<br />
<br />
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]<br />
<br />
===Spring 2016===<br />
[Feb 24] <font color="#ff0000"></font><br />
<br />
[Feb 17] <font color="#ff0000"></font><br />
<br />
[Feb 10] <font color="#ff0000"></font><br />
<br />
[Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]<br />
<br />
[Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]<br />
<br />
[Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]<br />
<br />
===Fall 2015===<br />
<br />
[Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]<br />
<br />
[Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]<br />
<br />
[Nov 26] Thanksgiving break<br />
<br />
[Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]<br />
<br />
Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]<br />
<br />
=== Summer 2014 ===<br />
<br />
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]<br />
<br />
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]<br />
<br />
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]<br />
<br />
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]<br />
<br />
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]<br />
<br />
=== Spring 2014 ===<br />
<br />
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]<br />
<br />
=== Fall 2013 ===<br />
<br />
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]<br />
<br />
=== Summer 2013 ===<br />
<br />
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]<br />
<br />
=== Spring 2013 ===<br />
<br />
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]<br />
<br />
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf] <br />
<br />
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]<br />
<br />
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]<br />
<br />
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]<br />
<br />
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]<br />
<br />
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]<br />
<br />
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]<br />
<br />
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]<br />
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]<br />
<br />
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]<br />
<br />
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]<br />
<br />
=== Fall 2012 ===<br />
<br />
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]<br />
<br />
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]<br />
<br />
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]<br />
<br />
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]<br />
<br />
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]<br />
<br />
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]<br />
<br />
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]<br />
<br />
=== Spring 2011 ===<br />
<br />
* [Feb 10] Anastassiou et al. Ephaptic coupling of cortical neurons [http://www.nature.com/neuro/journal/v14/n2/full/nn.2727.html]<br />
* [Feb 10] Jin et al. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex [http://www.nature.com/neuro/journal/v14/n2/full/nn.2729.html]<br />
<br />
* [Jan 20] A review of NIPS 2010 papers. [http://books.nips.cc/nips23.html]<br />
<br />
=== Fall 2010 ===<br />
<br />
* [Dec 9] Welling. Herding algorithms. [http://www.ics.uci.edu/%7Ewelling/publications/publications.html]<br />
<br />
* [Dec 2] Neal. MCMC using Hamiltonian dynamics. [http://www.cs.utoronto.ca/~radford/ftp/ham-mcmc.ps]<br />
<br />
* [Nov 18] Mairal et al. Task-driven dictionary learning. [http://arxiv.org/abs/1009.5358]<br />
<br />
* [Oct 21] Hamed. Self-referential dynamical systems for the self-organization of behavior in robotic systems. Ch 2-3 of [http://robot.informatik.uni-leipzig.de/research/publications/2007/ThesisHamed.pdf] <br />
<br />
* [Oct 14] Hammond, Vandergheynst, and Gribonval. Wavelets on graphs via spectral graph theory. [http://dx.doi.org/10.1016/j.acha.2010.04.005]<br />
<br />
* [Oct 7] Neal. Annealed importance sampling. [http://www.springerlink.com/content/x40w6w4r1142651k/]<br />
<br />
* [Sep 30] Martius, Herrmann. Taming the beast: Guided self-organization of behavior in autonomous robots. [http://www.springerlink.com/content/80013g12784261n3/]<br />
<br />
* [Sep 23] Bullier, Jean. "What is Fed Back?" in 23 Problems in Systems Neuroscience. [https://docs.google.com/fileview?id=0B30YK-ZtKoHXNGVlYjNlODUtNTYyOS00OTJmLWJjNWItNmUwNTljYmE3M2U4&hl=en&authkey=CMvu2LAM]<br />
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=== [[Past TCN Papers]] ===<br />
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=== Time and Location ===<br />
'''12:00-1:00pm''' every Thursday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on meeting dates.<br />
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=== Overview ===<br />
This journal club is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.<br />
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It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.<br />
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If you have questions, please email the club organizer James Arnemann.<br />
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=== E-mail List ===<br />
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To subscribe to the journal club email list, visit [https://calmail.berkeley.edu/manage/list/listinfo/redwood_tcn@lists.berkeley.edu link]. You will receive emails twice a week about papers that will be covered in the next meeting.<br />
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=== Guidelines for Presenting Papers ===<br />
Each person that selects a paper should present, in about 15-30 minutes:<br />
* an executive summary<br />
* an outline of the key points, ideas, or contributions<br />
* relevant background information<br />
* a description of the key figures<br />
* what you took away from the paper<br />
* some potential questions for discussion<br />
* you are encouraged to use whatever method to present (slides, puppets, etc.)<br />
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===[[Suggestion Board]]===</div>Dpaiton