TCN

Topics in Computational Neuroscience

For ideas about some interesting papers to discuss have look here TCN Paper Ideas

Overview

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.

It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.

If you have questions, please email the club organizer (Fall 2016) Sean Mackesey

Time and Location

(Fall 2016) 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.

Guidelines for Presenting Papers

Each person that selects a paper should present, in about 15-30 minutes:

• an executive summary
• an outline of the key points, ideas, or contributions
• relevant background information
• a description of the key figures
• what you took away from the paper
• some potential questions for discussion
• you are encouraged to use whatever method to present (slides, puppets, etc.)

E-mail List

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.

Fall 2016

• [Dec 12] Dylan Paiton - JR Golden et al (2016). Conjectures Regarding the Nonlinear Geometry of Visual Neurons. [1]
• [Dec 05] Dylan Paiton - Kraus et al (2013). Hippocampal "Time Cells": Time Versus Path Integration [2]
• [Nov 28] Chris Warner - Lazer et al. (2010). The coevolution of networks and political attitudes. [3]
• [Nov 21] Kohta Ishikawa - Do & Vetterli (2003). Framing pyramids. [4]
• [Nov 14] Charles Frye - Wainwright & Jordan. (2008). Graphical models, exponential families, and variational inference. [5]
• [Nov 07] Petr Jezek - Eslami et al (2016). Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. [6]
• [Nov 04] Yubei Chen - Wainwright & Jordan. (2008). Graphical models, exponential families, and variational inference. [7]
• [Oct 24] Ryan Zarcone - Weiss et al (2007). Learning Compressed Sensing. [8]
• [Oct 17] Neha Wadia - Rajan et al (2016). Recurrent network models of sequence generation and memory. [9]
• [Oct 10] Eric Weiss - Tutorial: Hyperdimensional Representations for Scene Analysis.
• [Oct 03] Vasha Dutell - Dong & Atick (1995). Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus. [10]
• [Sep 26] Jesse Livezey - Cheung et al (2016). The auditory representation of speech sounds in human motor cortex. [11]
• [Sep 19] Brian Cheung - Discussion: The Biological Plausibility of Backpropagation.
• [Sep 12] Alex Anderson - Jaderberg et al (2016). Decoupled neural interfaces using synthetic gradients. [12]
• [Sep 05] Charles Garfinkle - Palmer et al (2015). Predictive information in a sensory population. [13]

Summer 2016

• [August 31] Spencer Kent - Fu et al (2016). Occlusion boundary detection via deep exploration of context. [14]
• [August 17] Paxon Frady - Glasser, M. F., et al. (2016) A Multi-modal parcellation of human cerebral cortex. [15] and Huth, Alexander G., et al. (2016) Natural speech reveals the semantic maps that tile human cerebral cortex. [16]
• [August 10] Mayur Mudigonda - Salimans, Tim, Diederik P. Kingma, and Max Welling (2015). Markov chain Monte Carlo and variational inference: Bridging the gap. [17]
• [August 3] Pratik Sachdeva - Moreno-Bote, Rubén, et al. (2014). Information-limiting correlations. [18]
• [July 26] Shariq Mobin - Karklin, Yan, and Eero P. Simoncelli (2011). Efficient coding of natural images with a population of noisy linear-nonlinear neurons.[19]
• [July 20] Sean Mackesey - Buzsáki, György (2010). Neural syntax: cell assemblies, synapsembles, and readers. [20]
• [July 13] Katarina Slama and Vasha DuTell - Akam, Thomas, and Dimitri M. Kullmann (2010). Oscillations and filtering networks support flexible routing of information. [21]
• [July 6] Karl Zipser - Bojarski, Mariusz, et al (2016). End to End Learning for Self-Driving Cars. [22]
• [June 29] Ian Robertson - Hermans, M., & Van Vaerenbergh, T. (2015). Towards Trainable Media: Using Waves for Neural Network-Style Training. [23]
• [June 22] Group Discussion - Jonas, Eric, and Konrad Kording. (2016) Could a neuroscientist understand a microprocessor? [24]
• [June 15] Spencer Kent, Mayur Mudigonda, and Eric Weiss - Kulkarni, Tejas D., et al (2015). Picture: A probabilistic programming language for scene perception. [25]
• [June 8] Jesse Livezey - Bornschein, Jörg, Marc Henniges, and Jörg Lücke (2013). Are V1 simple cells optimized for visual occlusions? A comparative study. [26]
• [June 1] Yubei Chen - Y. Karklin & M. S. Lewicki (2003). Learning higher-order structures in natural images. [27]
• [May 25] Eric Dodds - Y. Karklin, C. Ekanadham, & E.P. Simoncelli (2012). Hierarchical spike coding of sound. [28]

Spring 2016

• [May 11] NIPS exchange

• [May 04] Chris Warner - MEJ Newman (2006). Finding Community Structure in Networks using the Eigenvectors of Matrices [29]

• [Apr 27] Mr. (Alex) Anderson - E Ahissar, A Arieli (2012). Seeing Via Miniature Eye Movements- A Dynamic Hypothesis for Vision [30]

• [Apr 20] Charles Frye, Ryan Zarcone, Brian Cheung - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [31]

• [Apr 13] Kohta Ishikawa - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [32]

• [Apr 06] Spencer Kent - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [33]

• [Mar 30] Guy Isley - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [34]

• [Mar 23] Brian Cheung - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [35]

• [Mar 16] Yubei Chen - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [36]

• [Mar 09] Sean Mackesey - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [37]

• [Mar 02] Charles Garfinkle - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [38]

• [Feb 24] Eric Weiss - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [39]

• [Feb 17] Paxon Frady - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [40]

• [Feb 10] Cancelled - EECS Colloquium

• [Feb 03] Charles Frye - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [41]

• [Jan 21] Jesse Livezy and Andrew Berger - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [42]

• [Jan 14] Daniel Toker - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [43]

Fall 2015

• [Dec 17] Charles Frye - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [44]

• [Dec 03] Omer Hazon - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [45]

• [Nov 26] Thanksgiving break

• [Nov 19] Eric Dodds - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [46]

• [Nov 12] Vasha Dutell - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [47]

Summer 2014

• [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [48]

• [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [49]

• [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [50]

• [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [51]

• [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [52]

Spring 2014

• [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [53]

Fall 2013

• [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [54]

Summer 2013

• [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [55]

Spring 2013

• [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [56] [57]

• [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [58]

• [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [59]

• [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [60]

• [Mar 6] Grady 2006 - Random Walks for Image Segmentation [61]

• [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [62]

• [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [63]

• [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [64][65]

• [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [66]

Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [67]

• [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [68] [69]

• [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [70]

Fall 2012

• [Aug 22] Maass et al. - Liquid State Computing [71] [72]

• [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [73]

• [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [74]

• [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [75]

• [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [76]

• [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [77]

• [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [78]