Here is my [http://redwood.berkeley.edu/w/images/7/76/DeWeese_cv_short.pdf short CV] and below is my publication list. Most papers are available here as PDFs.

'''Publications:'''
*40. D. Mandal and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/1/1e/Mandal_%26_DeWeese_PRE_JE_generalized_to_nonHamiltonian_dynamics_2016.pdf Nonequilibrium work energy relation for non-Hamiltonian dynamics.] Physical Review E. 93(4):042129. (2016).

*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).

*38. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Zulkowski_DeWeese_Driven_Quantum_Systems_PRE_2015.pdf Optimal protocols for slowly driven quantum systems.] Physical Review E. 92(3):032113. (2015).

*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [http://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).

*36. V.M. Carels and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b2/Carels_DeWeese_Neuron_Preview_Duan_et_al_2015_reprint.pdf Rats Exert Executive Control.] Neuron 86, pp. 1324-1326 (2015).

*35. J. Sohl-Dickstein, S. Teng, B. Gaub, C. Rodgers, C. Li, M. DeWeese, and N. Harper. [http://redwood.berkeley.edu/w/images/b/bb/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_no_marquee_preprint.pdf A device for human ultrasonic echolocation.] IEEE Transactions in Biomedical Engineering. 62(6):1526-1534 (2015).

*34. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf Optimal finite-time erasure of a classical bit.] Physical Review E. 89(5):052140 (2014).

*33. C.C. Rodgers and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/c5/Rodgers_and_DeWeese_rodent_auditory_stim_selection_A1_PFC_Neuron_2014_w_Sup_Info.pdf Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents.] Neuron, 82(5), p1157–1170. (2014). ([http://redwood.berkeley.edu/w/images/6/6e/Odoemene_and_Churchland_Neuron_Preview_of_Rodgers_and_DeWeese_2014.pdf Neuron Preview of this paper by Odoemene and Churchland])

*32. J. Sohl-Dickstein, M. Mudigonda, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a3/Sohl-Dickstein_Mudigonda_DeWeese_LAHMC_icml2014_final_reprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).

*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE. 8(12):e82754 (2013).

*30. J. Zylberberg and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).

*29. T. Hromádka, A.M. Zador, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf Up-states are rare in awake auditory cortex.] Journal of Neurophysiology. 109(8):1989-95. (2013).

*28. P. King, J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.] Journal of Neuroscience 33(13):5475–85 (2013).

*27. J. Zylberberg, D. Pfau, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments.] Physical Review E. 86(6):066112 (2012).

*26. P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf The geometry of thermodynamic control.] Physical Review E. 86(4):041148 (2012).

*25. N. Carlson, V.L. Ming, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.] Public Library of Science Computational Biology. 7(10):e1002250 (2012).

*24. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf New method for parameter estimation in probabilistic models: Minimum probability flow.] Physical Review Letters. 107(22):220601 (2011).

*23. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf Minimum Probability Flow Learning.] Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).

*22. J. Zylberberg, J.T. Murphy, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.] Public Library of Science Computational Biology. 7(10):e1002250 (2011).

*21. J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf How should prey animals respond to uncertain threats?] Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).

*20. M.A. Olshausen and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/00/Olshausen_DeWeese_Statistics_of_Style_Nature_2010.pdf Applied mathematics: The statistics of style.] Nature 463(7284), 1027-1028 (2010).

*19. Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf Millisecond-scale differences in neural activity in auditory cortex can drive decisions.] Nature Neuroscience 11, 1262-1263 (2008).

*18. T. Hromadka, M.R. DeWeese, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf Sparse representation of sounds in the unanesthetized auditory cortex.] PLoS Biol. 6, 124-137 (2008).

*17. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf Whole-Cell Recording In Vivo.] Chapter 6 in Current Protocols in Neuroscience. John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).

*16. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.] J. Neuroscience 26(47), 12206-12218 (2006).

*15. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/3/38/DeWeese_Zador_Nature_N%26V_2006.pdf Neurobiology: Efficiency Measures.] Nature 439(7079), 920-921 (2006).

*14. M.R. DeWeese, T. Hromádka, and A.M. Zador. [http://redwood.berkeley.edu/w/images/d/d6/Hromadka_DeWeese_Zador_Neuron_AC_Bandwidth_Neuron_2005.pdf Reliability and representational bandwidth in auditory cortex.] Neuron 48, 479-588 (2005).

*13. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/7/75/DeWeese_Zador_Neuron_Preview_2005.pdf Neural gallops across auditory streams.] Neuron 48, 5-7 (2005).

*12. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5a/DeWeese_and_Zador_variability_JNeurophys_2004.pdf Shared and private variability in the auditory cortex.] J. Neurophysiol. 92, 1840-1855 (2004).

*11. M.R. DeWeese and A.M. Zador. Binary coding in auditory cortex. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 15, 101 (2003).

*10. M.R. DeWeese, M. Wehr, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5d/DeWeese_Wehr_Zador_binary_spiking_A1_2003.pdf Binary spiking in auditory cortex.] J. Neurosci. 23, 7940-7949 (2003).

*9. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/0c/DeWeese_Optim_Prep_for_Studying_Opt_Neuron_2000.pdf An optimal preparation for studying optimization.] Neuron 26, 546-548 (2000).

*8. M.R. DeWeese and M. Meister. [http://redwood.berkeley.edu/w/images/8/80/DeWeese_Meister_Info_per_observation_Network_1999.pdf How to measure the information gained from one symbol.] Network 10, 325-340 (1999).

*7. G. Buracas, A.M. Zador, M.R. DeWeese, and T. Albright. [http://redwood.berkeley.edu/w/images/0/00/Buracas_Zador_DeWeese_Albright_Efficient_Discrimination_Neuron_1998.pdf Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex.] Neuron 20, 959-969 (1998).

*6. M.R. DeWeese and A. Zador. [http://redwood.berkeley.edu/w/images/2/26/DeWeese_Zador_adaptation_to_variance_Neural_Computation_2003.pdf Asymmetric dynamics in optimal variance adaptation.] Neural Computation 10, 1179-1202 (1998).

*5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).

*4. M. DeWeese. [http://redwood.berkeley.edu/w/images/1/14/DeWeese_NIPS_1996.pdf Optimization principles for the neural code.] In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 8, p. 281 (1996).

*3. W. Bialek and M. DeWeese. [http://redwood.berkeley.edu/w/images/5/5a/Bialek_DeWeese_Random_Switching_Optimal_PRL_1995.pdf Random switching and optimal processing in the perception of ambiguous signals.] Phys. Rev. Lett. 74, 3077-3080 (1995).

*2. M. DeWeese and W. Bialek. [http://redwood.berkeley.edu/w/images/8/87/DeWeese_Bialek_Info_Flow_in_Sensory_Neurons_Il_Nuovo_Cimento_1995.pdf Information flow in sensory neurons.] Il Nuovo Cimento A17, 733 (1995).

*1. W. Bialek, M. DeWeese, F. Rieke, and D. Warland. [http://redwood.berkeley.edu/w/images/3/32/Bialek_et_al_Bits_and_Brains_1993.PDF Bits and brains: information flow in the nervous system.] Physica A 200, 581-593 (1993).

File:Mandal & DeWeese PRE JE generalized to nonHamiltonian dynamics 2016.pdf

2016-06-22T06:24:20Z

Deweese:

Michael DeWeese

2016-06-22T06:23:07Z

Deweese:

Here is my [http://redwood.berkeley.edu/w/images/7/76/DeWeese_cv_short.pdf short CV] and below is my publication list. Most papers are available here as PDFs.

'''Publications:'''
*40. D. Mandal and M.R. DeWeese. [[File:x.pdf]] Nonequilibrium work energy relation for non-Hamiltonian dynamics. Physical Review E. 93(4):042129. (2016).

*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).

*38. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Zulkowski_DeWeese_Driven_Quantum_Systems_PRE_2015.pdf Optimal protocols for slowly driven quantum systems.] Physical Review E. 92(3):032113. (2015).

*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [http://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).

*36. V.M. Carels and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b2/Carels_DeWeese_Neuron_Preview_Duan_et_al_2015_reprint.pdf Rats Exert Executive Control.] Neuron 86, pp. 1324-1326 (2015).

*35. J. Sohl-Dickstein, S. Teng, B. Gaub, C. Rodgers, C. Li, M. DeWeese, and N. Harper. [http://redwood.berkeley.edu/w/images/b/bb/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_no_marquee_preprint.pdf A device for human ultrasonic echolocation.] IEEE Transactions in Biomedical Engineering. 62(6):1526-1534 (2015).

*34. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf Optimal finite-time erasure of a classical bit.] Physical Review E. 89(5):052140 (2014).

*33. C.C. Rodgers and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/c5/Rodgers_and_DeWeese_rodent_auditory_stim_selection_A1_PFC_Neuron_2014_w_Sup_Info.pdf Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents.] Neuron, 82(5), p1157–1170. (2014). ([http://redwood.berkeley.edu/w/images/6/6e/Odoemene_and_Churchland_Neuron_Preview_of_Rodgers_and_DeWeese_2014.pdf Neuron Preview of this paper by Odoemene and Churchland])

*32. J. Sohl-Dickstein, M. Mudigonda, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a3/Sohl-Dickstein_Mudigonda_DeWeese_LAHMC_icml2014_final_reprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).

*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE. 8(12):e82754 (2013).

*30. J. Zylberberg and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).

*29. T. Hromádka, A.M. Zador, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf Up-states are rare in awake auditory cortex.] Journal of Neurophysiology. 109(8):1989-95. (2013).

*28. P. King, J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.] Journal of Neuroscience 33(13):5475–85 (2013).

*27. J. Zylberberg, D. Pfau, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments.] Physical Review E. 86(6):066112 (2012).

*26. P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf The geometry of thermodynamic control.] Physical Review E. 86(4):041148 (2012).

*25. N. Carlson, V.L. Ming, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.] Public Library of Science Computational Biology. 7(10):e1002250 (2012).

*24. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf New method for parameter estimation in probabilistic models: Minimum probability flow.] Physical Review Letters. 107(22):220601 (2011).

*23. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf Minimum Probability Flow Learning.] Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).

*22. J. Zylberberg, J.T. Murphy, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.] Public Library of Science Computational Biology. 7(10):e1002250 (2011).

*21. J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf How should prey animals respond to uncertain threats?] Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).

*20. M.A. Olshausen and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/00/Olshausen_DeWeese_Statistics_of_Style_Nature_2010.pdf Applied mathematics: The statistics of style.] Nature 463(7284), 1027-1028 (2010).

*19. Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf Millisecond-scale differences in neural activity in auditory cortex can drive decisions.] Nature Neuroscience 11, 1262-1263 (2008).

*18. T. Hromadka, M.R. DeWeese, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf Sparse representation of sounds in the unanesthetized auditory cortex.] PLoS Biol. 6, 124-137 (2008).

*17. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf Whole-Cell Recording In Vivo.] Chapter 6 in Current Protocols in Neuroscience. John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).

*16. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.] J. Neuroscience 26(47), 12206-12218 (2006).

*15. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/3/38/DeWeese_Zador_Nature_N%26V_2006.pdf Neurobiology: Efficiency Measures.] Nature 439(7079), 920-921 (2006).

*14. M.R. DeWeese, T. Hromádka, and A.M. Zador. [http://redwood.berkeley.edu/w/images/d/d6/Hromadka_DeWeese_Zador_Neuron_AC_Bandwidth_Neuron_2005.pdf Reliability and representational bandwidth in auditory cortex.] Neuron 48, 479-588 (2005).

*13. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/7/75/DeWeese_Zador_Neuron_Preview_2005.pdf Neural gallops across auditory streams.] Neuron 48, 5-7 (2005).

*12. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5a/DeWeese_and_Zador_variability_JNeurophys_2004.pdf Shared and private variability in the auditory cortex.] J. Neurophysiol. 92, 1840-1855 (2004).

*11. M.R. DeWeese and A.M. Zador. Binary coding in auditory cortex. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 15, 101 (2003).

*10. M.R. DeWeese, M. Wehr, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5d/DeWeese_Wehr_Zador_binary_spiking_A1_2003.pdf Binary spiking in auditory cortex.] J. Neurosci. 23, 7940-7949 (2003).

*9. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/0c/DeWeese_Optim_Prep_for_Studying_Opt_Neuron_2000.pdf An optimal preparation for studying optimization.] Neuron 26, 546-548 (2000).

*8. M.R. DeWeese and M. Meister. [http://redwood.berkeley.edu/w/images/8/80/DeWeese_Meister_Info_per_observation_Network_1999.pdf How to measure the information gained from one symbol.] Network 10, 325-340 (1999).

*7. G. Buracas, A.M. Zador, M.R. DeWeese, and T. Albright. [http://redwood.berkeley.edu/w/images/0/00/Buracas_Zador_DeWeese_Albright_Efficient_Discrimination_Neuron_1998.pdf Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex.] Neuron 20, 959-969 (1998).

*6. M.R. DeWeese and A. Zador. [http://redwood.berkeley.edu/w/images/2/26/DeWeese_Zador_adaptation_to_variance_Neural_Computation_2003.pdf Asymmetric dynamics in optimal variance adaptation.] Neural Computation 10, 1179-1202 (1998).

*5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).

*4. M. DeWeese. [http://redwood.berkeley.edu/w/images/1/14/DeWeese_NIPS_1996.pdf Optimization principles for the neural code.] In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 8, p. 281 (1996).

*3. W. Bialek and M. DeWeese. [http://redwood.berkeley.edu/w/images/5/5a/Bialek_DeWeese_Random_Switching_Optimal_PRL_1995.pdf Random switching and optimal processing in the perception of ambiguous signals.] Phys. Rev. Lett. 74, 3077-3080 (1995).

*2. M. DeWeese and W. Bialek. [http://redwood.berkeley.edu/w/images/8/87/DeWeese_Bialek_Info_Flow_in_Sensory_Neurons_Il_Nuovo_Cimento_1995.pdf Information flow in sensory neurons.] Il Nuovo Cimento A17, 733 (1995).

*1. W. Bialek, M. DeWeese, F. Rieke, and D. Warland. [http://redwood.berkeley.edu/w/images/3/32/Bialek_et_al_Bits_and_Brains_1993.PDF Bits and brains: information flow in the nervous system.] Physica A 200, 581-593 (1993).

Michael DeWeese

2016-06-22T06:21:55Z

Deweese:

Here is my [http://redwood.berkeley.edu/w/images/7/76/DeWeese_cv_short.pdf short CV] and below is my publication list. Most papers are available here as PDFs.

'''Publications:'''
*40. D. Mandal and M.R. DeWeese. [[File:x.pdf] Nonequilibrium work energy relation for non-Hamiltonian dynamics.] Physical Review E 93 (4), 042129. (2016).

*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).

*38. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Zulkowski_DeWeese_Driven_Quantum_Systems_PRE_2015.pdf Optimal protocols for slowly driven quantum systems.] Physical Review E. 92(3):032113. (2015).

*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [http://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).

*36. V.M. Carels and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b2/Carels_DeWeese_Neuron_Preview_Duan_et_al_2015_reprint.pdf Rats Exert Executive Control.] Neuron 86, pp. 1324-1326 (2015).

*35. J. Sohl-Dickstein, S. Teng, B. Gaub, C. Rodgers, C. Li, M. DeWeese, and N. Harper. [http://redwood.berkeley.edu/w/images/b/bb/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_no_marquee_preprint.pdf A device for human ultrasonic echolocation.] IEEE Transactions in Biomedical Engineering. 62(6):1526-1534 (2015).

*34. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf Optimal finite-time erasure of a classical bit.] Physical Review E. 89(5):052140 (2014).

*33. C.C. Rodgers and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/c5/Rodgers_and_DeWeese_rodent_auditory_stim_selection_A1_PFC_Neuron_2014_w_Sup_Info.pdf Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents.] Neuron, 82(5), p1157–1170. (2014). ([http://redwood.berkeley.edu/w/images/6/6e/Odoemene_and_Churchland_Neuron_Preview_of_Rodgers_and_DeWeese_2014.pdf Neuron Preview of this paper by Odoemene and Churchland])

*32. J. Sohl-Dickstein, M. Mudigonda, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a3/Sohl-Dickstein_Mudigonda_DeWeese_LAHMC_icml2014_final_reprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).

*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE. 8(12):e82754 (2013).

*30. J. Zylberberg and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).

*29. T. Hromádka, A.M. Zador, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf Up-states are rare in awake auditory cortex.] Journal of Neurophysiology. 109(8):1989-95. (2013).

*28. P. King, J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.] Journal of Neuroscience 33(13):5475–85 (2013).

*27. J. Zylberberg, D. Pfau, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments.] Physical Review E. 86(6):066112 (2012).

*26. P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf The geometry of thermodynamic control.] Physical Review E. 86(4):041148 (2012).

*25. N. Carlson, V.L. Ming, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.] Public Library of Science Computational Biology. 7(10):e1002250 (2012).

*24. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf New method for parameter estimation in probabilistic models: Minimum probability flow.] Physical Review Letters. 107(22):220601 (2011).

*23. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf Minimum Probability Flow Learning.] Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).

*22. J. Zylberberg, J.T. Murphy, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.] Public Library of Science Computational Biology. 7(10):e1002250 (2011).

*21. J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf How should prey animals respond to uncertain threats?] Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).

*20. M.A. Olshausen and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/00/Olshausen_DeWeese_Statistics_of_Style_Nature_2010.pdf Applied mathematics: The statistics of style.] Nature 463(7284), 1027-1028 (2010).

*19. Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf Millisecond-scale differences in neural activity in auditory cortex can drive decisions.] Nature Neuroscience 11, 1262-1263 (2008).

*18. T. Hromadka, M.R. DeWeese, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf Sparse representation of sounds in the unanesthetized auditory cortex.] PLoS Biol. 6, 124-137 (2008).

*17. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf Whole-Cell Recording In Vivo.] Chapter 6 in Current Protocols in Neuroscience. John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).

*16. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.] J. Neuroscience 26(47), 12206-12218 (2006).

*15. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/3/38/DeWeese_Zador_Nature_N%26V_2006.pdf Neurobiology: Efficiency Measures.] Nature 439(7079), 920-921 (2006).

*14. M.R. DeWeese, T. Hromádka, and A.M. Zador. [http://redwood.berkeley.edu/w/images/d/d6/Hromadka_DeWeese_Zador_Neuron_AC_Bandwidth_Neuron_2005.pdf Reliability and representational bandwidth in auditory cortex.] Neuron 48, 479-588 (2005).

*13. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/7/75/DeWeese_Zador_Neuron_Preview_2005.pdf Neural gallops across auditory streams.] Neuron 48, 5-7 (2005).

*12. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5a/DeWeese_and_Zador_variability_JNeurophys_2004.pdf Shared and private variability in the auditory cortex.] J. Neurophysiol. 92, 1840-1855 (2004).

*11. M.R. DeWeese and A.M. Zador. Binary coding in auditory cortex. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 15, 101 (2003).

*10. M.R. DeWeese, M. Wehr, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5d/DeWeese_Wehr_Zador_binary_spiking_A1_2003.pdf Binary spiking in auditory cortex.] J. Neurosci. 23, 7940-7949 (2003).

*9. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/0c/DeWeese_Optim_Prep_for_Studying_Opt_Neuron_2000.pdf An optimal preparation for studying optimization.] Neuron 26, 546-548 (2000).

*8. M.R. DeWeese and M. Meister. [http://redwood.berkeley.edu/w/images/8/80/DeWeese_Meister_Info_per_observation_Network_1999.pdf How to measure the information gained from one symbol.] Network 10, 325-340 (1999).

*7. G. Buracas, A.M. Zador, M.R. DeWeese, and T. Albright. [http://redwood.berkeley.edu/w/images/0/00/Buracas_Zador_DeWeese_Albright_Efficient_Discrimination_Neuron_1998.pdf Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex.] Neuron 20, 959-969 (1998).

*6. M.R. DeWeese and A. Zador. [http://redwood.berkeley.edu/w/images/2/26/DeWeese_Zador_adaptation_to_variance_Neural_Computation_2003.pdf Asymmetric dynamics in optimal variance adaptation.] Neural Computation 10, 1179-1202 (1998).

*5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).

*4. M. DeWeese. [http://redwood.berkeley.edu/w/images/1/14/DeWeese_NIPS_1996.pdf Optimization principles for the neural code.] In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 8, p. 281 (1996).

*3. W. Bialek and M. DeWeese. [http://redwood.berkeley.edu/w/images/5/5a/Bialek_DeWeese_Random_Switching_Optimal_PRL_1995.pdf Random switching and optimal processing in the perception of ambiguous signals.] Phys. Rev. Lett. 74, 3077-3080 (1995).

*2. M. DeWeese and W. Bialek. [http://redwood.berkeley.edu/w/images/8/87/DeWeese_Bialek_Info_Flow_in_Sensory_Neurons_Il_Nuovo_Cimento_1995.pdf Information flow in sensory neurons.] Il Nuovo Cimento A17, 733 (1995).

*1. W. Bialek, M. DeWeese, F. Rieke, and D. Warland. [http://redwood.berkeley.edu/w/images/3/32/Bialek_et_al_Bits_and_Brains_1993.PDF Bits and brains: information flow in the nervous system.] Physica A 200, 581-593 (1993).

Michael DeWeese

2015-12-22T06:22:12Z

Deweese:

Here is my [http://redwood.berkeley.edu/w/images/7/76/DeWeese_cv_short.pdf short CV] and below is my publication list. Most papers are available here as PDFs.

'''Publications:'''
*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).

*38. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Zulkowski_DeWeese_Driven_Quantum_Systems_PRE_2015.pdf Optimal protocols for slowly driven quantum systems.] Physical Review E. 92(3):032113. (2015).

*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [http://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).

*36. V.M. Carels and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b2/Carels_DeWeese_Neuron_Preview_Duan_et_al_2015_reprint.pdf Rats Exert Executive Control.] Neuron 86, pp. 1324-1326 (2015).

*35. J. Sohl-Dickstein, S. Teng, B. Gaub, C. Rodgers, C. Li, M. DeWeese, and N. Harper. [http://redwood.berkeley.edu/w/images/b/bb/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_no_marquee_preprint.pdf A device for human ultrasonic echolocation.] IEEE Transactions in Biomedical Engineering. 62(6):1526-1534 (2015).

*34. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf Optimal finite-time erasure of a classical bit.] Physical Review E. 89(5):052140 (2014).

*33. C.C. Rodgers and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/c5/Rodgers_and_DeWeese_rodent_auditory_stim_selection_A1_PFC_Neuron_2014_w_Sup_Info.pdf Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents.] Neuron, 82(5), p1157–1170. (2014). ([http://redwood.berkeley.edu/w/images/6/6e/Odoemene_and_Churchland_Neuron_Preview_of_Rodgers_and_DeWeese_2014.pdf Neuron Preview of this paper by Odoemene and Churchland])

*32. J. Sohl-Dickstein, M. Mudigonda, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a3/Sohl-Dickstein_Mudigonda_DeWeese_LAHMC_icml2014_final_reprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).

*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE. 8(12):e82754 (2013).

*30. J. Zylberberg and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).

*29. T. Hromádka, A.M. Zador, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf Up-states are rare in awake auditory cortex.] Journal of Neurophysiology. 109(8):1989-95. (2013).

*28. P. King, J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.] Journal of Neuroscience 33(13):5475–85 (2013).

*27. J. Zylberberg, D. Pfau, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments.] Physical Review E. 86(6):066112 (2012).

*26. P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf The geometry of thermodynamic control.] Physical Review E. 86(4):041148 (2012).

*25. N. Carlson, V.L. Ming, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.] Public Library of Science Computational Biology. 7(10):e1002250 (2012).

*24. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf New method for parameter estimation in probabilistic models: Minimum probability flow.] Physical Review Letters. 107(22):220601 (2011).

*23. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf Minimum Probability Flow Learning.] Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).

*22. J. Zylberberg, J.T. Murphy, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.] Public Library of Science Computational Biology. 7(10):e1002250 (2011).

*21. J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf How should prey animals respond to uncertain threats?] Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).

*20. M.A. Olshausen and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/00/Olshausen_DeWeese_Statistics_of_Style_Nature_2010.pdf Applied mathematics: The statistics of style.] Nature 463(7284), 1027-1028 (2010).

*19. Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf Millisecond-scale differences in neural activity in auditory cortex can drive decisions.] Nature Neuroscience 11, 1262-1263 (2008).

*18. T. Hromadka, M.R. DeWeese, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf Sparse representation of sounds in the unanesthetized auditory cortex.] PLoS Biol. 6, 124-137 (2008).

*17. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf Whole-Cell Recording In Vivo.] Chapter 6 in Current Protocols in Neuroscience. John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).

*16. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.] J. Neuroscience 26(47), 12206-12218 (2006).

*15. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/3/38/DeWeese_Zador_Nature_N%26V_2006.pdf Neurobiology: Efficiency Measures.] Nature 439(7079), 920-921 (2006).

*14. M.R. DeWeese, T. Hromádka, and A.M. Zador. [http://redwood.berkeley.edu/w/images/d/d6/Hromadka_DeWeese_Zador_Neuron_AC_Bandwidth_Neuron_2005.pdf Reliability and representational bandwidth in auditory cortex.] Neuron 48, 479-588 (2005).

*13. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/7/75/DeWeese_Zador_Neuron_Preview_2005.pdf Neural gallops across auditory streams.] Neuron 48, 5-7 (2005).

*12. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5a/DeWeese_and_Zador_variability_JNeurophys_2004.pdf Shared and private variability in the auditory cortex.] J. Neurophysiol. 92, 1840-1855 (2004).

*11. M.R. DeWeese and A.M. Zador. Binary coding in auditory cortex. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 15, 101 (2003).

*10. M.R. DeWeese, M. Wehr, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5d/DeWeese_Wehr_Zador_binary_spiking_A1_2003.pdf Binary spiking in auditory cortex.] J. Neurosci. 23, 7940-7949 (2003).

*9. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/0c/DeWeese_Optim_Prep_for_Studying_Opt_Neuron_2000.pdf An optimal preparation for studying optimization.] Neuron 26, 546-548 (2000).

*8. M.R. DeWeese and M. Meister. [http://redwood.berkeley.edu/w/images/8/80/DeWeese_Meister_Info_per_observation_Network_1999.pdf How to measure the information gained from one symbol.] Network 10, 325-340 (1999).

*7. G. Buracas, A.M. Zador, M.R. DeWeese, and T. Albright. [http://redwood.berkeley.edu/w/images/0/00/Buracas_Zador_DeWeese_Albright_Efficient_Discrimination_Neuron_1998.pdf Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex.] Neuron 20, 959-969 (1998).

*6. M.R. DeWeese and A. Zador. [http://redwood.berkeley.edu/w/images/2/26/DeWeese_Zador_adaptation_to_variance_Neural_Computation_2003.pdf Asymmetric dynamics in optimal variance adaptation.] Neural Computation 10, 1179-1202 (1998).

*5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).

*4. M. DeWeese. [http://redwood.berkeley.edu/w/images/1/14/DeWeese_NIPS_1996.pdf Optimization principles for the neural code.] In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 8, p. 281 (1996).

*3. W. Bialek and M. DeWeese. [http://redwood.berkeley.edu/w/images/5/5a/Bialek_DeWeese_Random_Switching_Optimal_PRL_1995.pdf Random switching and optimal processing in the perception of ambiguous signals.] Phys. Rev. Lett. 74, 3077-3080 (1995).

*2. M. DeWeese and W. Bialek. [http://redwood.berkeley.edu/w/images/8/87/DeWeese_Bialek_Info_Flow_in_Sensory_Neurons_Il_Nuovo_Cimento_1995.pdf Information flow in sensory neurons.] Il Nuovo Cimento A17, 733 (1995).

*1. W. Bialek, M. DeWeese, F. Rieke, and D. Warland. [http://redwood.berkeley.edu/w/images/3/32/Bialek_et_al_Bits_and_Brains_1993.PDF Bits and brains: information flow in the nervous system.] Physica A 200, 581-593 (1993).

2015-10-24T20:51:46Z

Deweese:

Michael DeWeese

2015-10-24T20:49:01Z

Deweese:

Here is my [http://redwood.berkeley.edu/w/images/7/76/DeWeese_cv_short.pdf short CV] and below is my publication list. Most papers are available here as PDFs.

'''Publications:'''
*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).

*38. P.R. Zulkowski and M.R. DeWeese. [[File:x.pdf]] Optimal protocols for slowly driven quantum systems. Physical Review E. 92(3):032113. (2015).

*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [http://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).

*36. V.M. Carels and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b2/Carels_DeWeese_Neuron_Preview_Duan_et_al_2015_reprint.pdf Rats Exert Executive Control.] Neuron 86, pp. 1324-1326 (2015).

*35. J. Sohl-Dickstein, S. Teng, B. Gaub, C. Rodgers, C. Li, M. DeWeese, and N. Harper. [http://redwood.berkeley.edu/w/images/b/bb/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_no_marquee_preprint.pdf A device for human ultrasonic echolocation.] IEEE Transactions in Biomedical Engineering. 62(6):1526-1534 (2015).

*34. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf Optimal finite-time erasure of a classical bit.] Physical Review E. 89(5):052140 (2014).

*33. C.C. Rodgers and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/c5/Rodgers_and_DeWeese_rodent_auditory_stim_selection_A1_PFC_Neuron_2014_w_Sup_Info.pdf Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents.] Neuron, 82(5), p1157–1170. (2014). ([http://redwood.berkeley.edu/w/images/6/6e/Odoemene_and_Churchland_Neuron_Preview_of_Rodgers_and_DeWeese_2014.pdf Neuron Preview of this paper by Odoemene and Churchland])

*32. J. Sohl-Dickstein, M. Mudigonda, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a3/Sohl-Dickstein_Mudigonda_DeWeese_LAHMC_icml2014_final_reprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).

*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE. 8(12):e82754 (2013).

*30. J. Zylberberg and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).

*29. T. Hromádka, A.M. Zador, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf Up-states are rare in awake auditory cortex.] Journal of Neurophysiology. 109(8):1989-95. (2013).

*28. P. King, J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.] Journal of Neuroscience 33(13):5475–85 (2013).

*27. J. Zylberberg, D. Pfau, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments.] Physical Review E. 86(6):066112 (2012).

*26. P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf The geometry of thermodynamic control.] Physical Review E. 86(4):041148 (2012).

*25. N. Carlson, V.L. Ming, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.] Public Library of Science Computational Biology. 7(10):e1002250 (2012).

*24. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf New method for parameter estimation in probabilistic models: Minimum probability flow.] Physical Review Letters. 107(22):220601 (2011).

*23. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf Minimum Probability Flow Learning.] Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).

*22. J. Zylberberg, J.T. Murphy, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.] Public Library of Science Computational Biology. 7(10):e1002250 (2011).

*21. J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf How should prey animals respond to uncertain threats?] Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).

*20. M.A. Olshausen and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/00/Olshausen_DeWeese_Statistics_of_Style_Nature_2010.pdf Applied mathematics: The statistics of style.] Nature 463(7284), 1027-1028 (2010).

*19. Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf Millisecond-scale differences in neural activity in auditory cortex can drive decisions.] Nature Neuroscience 11, 1262-1263 (2008).

*18. T. Hromadka, M.R. DeWeese, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf Sparse representation of sounds in the unanesthetized auditory cortex.] PLoS Biol. 6, 124-137 (2008).

*17. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf Whole-Cell Recording In Vivo.] Chapter 6 in Current Protocols in Neuroscience. John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).

*16. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.] J. Neuroscience 26(47), 12206-12218 (2006).

*15. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/3/38/DeWeese_Zador_Nature_N%26V_2006.pdf Neurobiology: Efficiency Measures.] Nature 439(7079), 920-921 (2006).

*14. M.R. DeWeese, T. Hromádka, and A.M. Zador. [http://redwood.berkeley.edu/w/images/d/d6/Hromadka_DeWeese_Zador_Neuron_AC_Bandwidth_Neuron_2005.pdf Reliability and representational bandwidth in auditory cortex.] Neuron 48, 479-588 (2005).

*13. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/7/75/DeWeese_Zador_Neuron_Preview_2005.pdf Neural gallops across auditory streams.] Neuron 48, 5-7 (2005).

*12. M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5a/DeWeese_and_Zador_variability_JNeurophys_2004.pdf Shared and private variability in the auditory cortex.] J. Neurophysiol. 92, 1840-1855 (2004).

*11. M.R. DeWeese and A.M. Zador. Binary coding in auditory cortex. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 15, 101 (2003).

*10. M.R. DeWeese, M. Wehr, and A.M. Zador. [http://redwood.berkeley.edu/w/images/5/5d/DeWeese_Wehr_Zador_binary_spiking_A1_2003.pdf Binary spiking in auditory cortex.] J. Neurosci. 23, 7940-7949 (2003).

*9. M.R. DeWeese. [http://redwood.berkeley.edu/w/images/0/0c/DeWeese_Optim_Prep_for_Studying_Opt_Neuron_2000.pdf An optimal preparation for studying optimization.] Neuron 26, 546-548 (2000).

*8. M.R. DeWeese and M. Meister. [http://redwood.berkeley.edu/w/images/8/80/DeWeese_Meister_Info_per_observation_Network_1999.pdf How to measure the information gained from one symbol.] Network 10, 325-340 (1999).

*7. G. Buracas, A.M. Zador, M.R. DeWeese, and T. Albright. [http://redwood.berkeley.edu/w/images/0/00/Buracas_Zador_DeWeese_Albright_Efficient_Discrimination_Neuron_1998.pdf Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex.] Neuron 20, 959-969 (1998).

*6. M.R. DeWeese and A. Zador. [http://redwood.berkeley.edu/w/images/2/26/DeWeese_Zador_adaptation_to_variance_Neural_Computation_2003.pdf Asymmetric dynamics in optimal variance adaptation.] Neural Computation 10, 1179-1202 (1998).

*5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).

*4. M. DeWeese. [http://redwood.berkeley.edu/w/images/1/14/DeWeese_NIPS_1996.pdf Optimization principles for the neural code.] In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 8, p. 281 (1996).

*3. W. Bialek and M. DeWeese. [http://redwood.berkeley.edu/w/images/5/5a/Bialek_DeWeese_Random_Switching_Optimal_PRL_1995.pdf Random switching and optimal processing in the perception of ambiguous signals.] Phys. Rev. Lett. 74, 3077-3080 (1995).

*2. M. DeWeese and W. Bialek. [http://redwood.berkeley.edu/w/images/8/87/DeWeese_Bialek_Info_Flow_in_Sensory_Neurons_Il_Nuovo_Cimento_1995.pdf Information flow in sensory neurons.] Il Nuovo Cimento A17, 733 (1995).

*1. W. Bialek, M. DeWeese, F. Rieke, and D. Warland. [http://redwood.berkeley.edu/w/images/3/32/Bialek_et_al_Bits_and_Brains_1993.PDF Bits and brains: information flow in the nervous system.] Physica A 200, 581-593 (1993).

Michael DeWeese

2015-10-24T20:45:33Z

Deweese:

Here is my [https://redwood.berkeley.edu/w/images/7/76/DeWeese_cv_short.pdf short CV] and below is my publication list. Most papers are available here as PDFs.

'''Publications:'''
*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).

*38. P.R. Zulkowski and M.R. DeWeese. Optimal protocols for slowly driven quantum systems. Physical Review E. 92(3):032113. (2015).

*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [https://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).

*36. V.M. Carels and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/b/b2/Carels_DeWeese_Neuron_Preview_Duan_et_al_2015_reprint.pdf Rats Exert Executive Control.] Neuron 86, pp. 1324-1326 (2015).

*35. J. Sohl-Dickstein, S. Teng, B. Gaub, C. Rodgers, C. Li, M. DeWeese, and N. Harper. [https://redwood.berkeley.edu/w/images/b/bb/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_no_marquee_preprint.pdf A device for human ultrasonic echolocation.] IEEE Transactions in Biomedical Engineering. 62(6):1526-1534 (2015).

*34. P.R. Zulkowski and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf Optimal finite-time erasure of a classical bit.] Physical Review E. 89(5):052140 (2014).

*33. C.C. Rodgers and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/c/c5/Rodgers_and_DeWeese_rodent_auditory_stim_selection_A1_PFC_Neuron_2014_w_Sup_Info.pdf Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents.] Neuron, 82(5), p1157–1170. (2014). ([https://redwood.berkeley.edu/w/images/6/6e/Odoemene_and_Churchland_Neuron_Preview_of_Rodgers_and_DeWeese_2014.pdf Neuron Preview of this paper by Odoemene and Churchland])

*32. J. Sohl-Dickstein, M. Mudigonda, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/a/a3/Sohl-Dickstein_Mudigonda_DeWeese_LAHMC_icml2014_final_reprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).

*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE. 8(12):e82754 (2013).

*30. J. Zylberberg and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).

*29. T. Hromádka, A.M. Zador, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf Up-states are rare in awake auditory cortex.] Journal of Neurophysiology. 109(8):1989-95. (2013).

*28. P. King, J. Zylberberg, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.] Journal of Neuroscience 33(13):5475–85 (2013).

*27. J. Zylberberg, D. Pfau, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments.] Physical Review E. 86(6):066112 (2012).

*26. P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf The geometry of thermodynamic control.] Physical Review E. 86(4):041148 (2012).

*25. N. Carlson, V.L. Ming, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.] Public Library of Science Computational Biology. 7(10):e1002250 (2012).

*24. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf New method for parameter estimation in probabilistic models: Minimum probability flow.] Physical Review Letters. 107(22):220601 (2011).

*23. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf Minimum Probability Flow Learning.] Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).

*22. J. Zylberberg, J.T. Murphy, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.] Public Library of Science Computational Biology. 7(10):e1002250 (2011).

*21. J. Zylberberg, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf How should prey animals respond to uncertain threats?] Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).

*20. M.A. Olshausen and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/0/00/Olshausen_DeWeese_Statistics_of_Style_Nature_2010.pdf Applied mathematics: The statistics of style.] Nature 463(7284), 1027-1028 (2010).

*19. Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador. [https://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf Millisecond-scale differences in neural activity in auditory cortex can drive decisions.] Nature Neuroscience 11, 1262-1263 (2008).

*18. T. Hromadka, M.R. DeWeese, and A.M. Zador. [https://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf Sparse representation of sounds in the unanesthetized auditory cortex.] PLoS Biol. 6, 124-137 (2008).

*17. M.R. DeWeese. [https://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf Whole-Cell Recording In Vivo.] Chapter 6 in Current Protocols in Neuroscience. John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).

*16. M.R. DeWeese and A.M. Zador. [https://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.] J. Neuroscience 26(47), 12206-12218 (2006).

*15. M.R. DeWeese and A.M. Zador. [https://redwood.berkeley.edu/w/images/3/38/DeWeese_Zador_Nature_N%26V_2006.pdf Neurobiology: Efficiency Measures.] Nature 439(7079), 920-921 (2006).

*14. M.R. DeWeese, T. Hromádka, and A.M. Zador. [https://redwood.berkeley.edu/w/images/d/d6/Hromadka_DeWeese_Zador_Neuron_AC_Bandwidth_Neuron_2005.pdf Reliability and representational bandwidth in auditory cortex.] Neuron 48, 479-588 (2005).

*13. M.R. DeWeese and A.M. Zador. [https://redwood.berkeley.edu/w/images/7/75/DeWeese_Zador_Neuron_Preview_2005.pdf Neural gallops across auditory streams.] Neuron 48, 5-7 (2005).

*12. M.R. DeWeese and A.M. Zador. [https://redwood.berkeley.edu/w/images/5/5a/DeWeese_and_Zador_variability_JNeurophys_2004.pdf Shared and private variability in the auditory cortex.] J. Neurophysiol. 92, 1840-1855 (2004).

*11. M.R. DeWeese and A.M. Zador. Binary coding in auditory cortex. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 15, 101 (2003).

*10. M.R. DeWeese, M. Wehr, and A.M. Zador. [https://redwood.berkeley.edu/w/images/5/5d/DeWeese_Wehr_Zador_binary_spiking_A1_2003.pdf Binary spiking in auditory cortex.] J. Neurosci. 23, 7940-7949 (2003).

*9. M.R. DeWeese. [https://redwood.berkeley.edu/w/images/0/0c/DeWeese_Optim_Prep_for_Studying_Opt_Neuron_2000.pdf An optimal preparation for studying optimization.] Neuron 26, 546-548 (2000).

*8. M.R. DeWeese and M. Meister. [https://redwood.berkeley.edu/w/images/8/80/DeWeese_Meister_Info_per_observation_Network_1999.pdf How to measure the information gained from one symbol.] Network 10, 325-340 (1999).

*7. G. Buracas, A.M. Zador, M.R. DeWeese, and T. Albright. [https://redwood.berkeley.edu/w/images/0/00/Buracas_Zador_DeWeese_Albright_Efficient_Discrimination_Neuron_1998.pdf Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex.] Neuron 20, 959-969 (1998).

*6. M.R. DeWeese and A. Zador. [https://redwood.berkeley.edu/w/images/2/26/DeWeese_Zador_adaptation_to_variance_Neural_Computation_2003.pdf Asymmetric dynamics in optimal variance adaptation.] Neural Computation 10, 1179-1202 (1998).

*5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).

*4. M. DeWeese. [https://redwood.berkeley.edu/w/images/1/14/DeWeese_NIPS_1996.pdf Optimization principles for the neural code.] In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 8, p. 281 (1996).

*3. W. Bialek and M. DeWeese. [https://redwood.berkeley.edu/w/images/5/5a/Bialek_DeWeese_Random_Switching_Optimal_PRL_1995.pdf Random switching and optimal processing in the perception of ambiguous signals.] Phys. Rev. Lett. 74, 3077-3080 (1995).

*2. M. DeWeese and W. Bialek. [https://redwood.berkeley.edu/w/images/8/87/DeWeese_Bialek_Info_Flow_in_Sensory_Neurons_Il_Nuovo_Cimento_1995.pdf Information flow in sensory neurons.] Il Nuovo Cimento A17, 733 (1995).

*1. W. Bialek, M. DeWeese, F. Rieke, and D. Warland. [https://redwood.berkeley.edu/w/images/3/32/Bialek_et_al_Bits_and_Brains_1993.PDF Bits and brains: information flow in the nervous system.] Physica A 200, 581-593 (1993).

2015-03-14T22:33:29Z

Deweese:

__NOEDITSECTION__
== Journal Papers and reviewed Bookchapters ==
'''2015'''

J. Sohl-Dickstein, S. Teng, C. Rodgers, M.R. DeWeese, and N. Harper. [https://redwood.berkeley.edu/w/images/b/bb/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_no_marquee_preprint.pdf A device for human ultrasonic echolocation.] IEEE Transactions on Biomedical Engineering (in press) (2015)
'''2014'''

J. Sohl-Dickstein, M. Mudigonda, M. Deweese (2014) Hamiltonian Monte Carlo Without Detailed Balance. ICML (2014)
[http://jmlr.org/proceedings/papers/v32/sohl-dickstein14.pdf pdf]

P. R. Zulkowski and M. R. DeWeese. (2014) Optimal finite-time erasure of a classical bit. Physical Review E. 89(5):052140.
[https://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf pdf]

G. Agarwal, I. H. Stevenson, A. Berényi, K. Mizuseki, G. Buzsáki, F. T. Sommer: Spatially distributed local fields in the hippocampus encode rat position. Science 344 (2014): 626-630. [http://redwood.berkeley.edu/w/images/2/24/AgarwalSommer.pdf pdf] [http://redwood.berkeley.edu/w/images/c/c4/AgarwalSommerSupp.pdf Supplement]

J. A. Hirsch, X. Wang, V. Vaingankar, F. T. Sommer: Inhibitory circuits in the visual thalamus. Chapter in: The New Visual Neurosciences, Eds.: Leo M. Chalupa and John S. Werner, MIT Press (2014)

A. Knoblauch, E. Koerner, U. Koerner, F. T. Sommer: Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect. PLOS ONE (2014) in the press

L. M. Martinez, M. Molano-Mazon, X. Wang, F. T. Sommer, J. A. Hirsch: Statistical wiring of thalamic receptive fields optimizes spatial sampling of the retinal image. Neuron 81 (2014) 943-956

C. Rodgers and M. R. DeWeese. Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents. Neuron. (in press) (2014). [https://redwood.berkeley.edu/w/images/c/ca/Rogers_deweese_2014.pdf pdf]

F. T. Sommer: Neural oscillatons and synchrony as mechanisms for coding, communication and computation in the visual system. Chapter in: The New Visual Neurosciences, Eds.: Leo M. Chalupa and John S. Werner, MIT Press (2014)

'''2013'''

T. Hromádka, A.M. Zador, and M. R. DeWeese. (2013) Up-states are rare in awake auditory cortex. Journal of Neurophysiology, 109(8):1989-95. [https://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf pdf]

P. King, J. Zylberberg, and M. R. DeWeese. (2013) Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1. Journal of Neuroscience, 33(13):5475–85. [https://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf pdf]

D. Y. Little, F. T. Sommer: Learning and exploration in action-perception loops. Frontiers in Neural Circuits, 22 March 2013 doi: 10.3389/fncir.2013.00037 online

D. Y. Little, F. T. Sommer: Maximal mutual information, not minimal entropy, for escaping the "Dark Room". Comment on "Whatever next? Predictive brains, situated agents, and the future of cognitive science." in Behavioral Brain Sciences 2013 Jun;36(3):220-221. doi: 10.1017/S0140525X12002415 [1]

P. R. Zulkowski, D. A. Sivak, and M. R. DeWeese. Optimal control of transitions between nonequilibrium steady states. Public Library of Science ONE. 8(12):e82754 (2013). [https://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf pdf]

J. Zylberberg and M. R. DeWeese. (2013) Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. PLoS Computational Biology. 9(8):e1003182.
[https://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf pdf]

'''2012'''

J. Zylberberg, D. Pfau, and M. R. DeWeese. (2012) Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments. Physical Review E, 86:066112. [https://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf pdf]

P. R. Zulkowski, D. A. Sivak, G. E. Crooks, and M. R. DeWeese. (2012) The geometry of thermodynamic control. Physical Review E, 86(4 Pt 1):041148. [https://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf pdf]

N. Carlson, V. L. Ming, and M. R. DeWeese. (2012) Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus. PLoS Computational Biology, 7(10):e1002250. [https://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf pdf]

'''2011'''

J. Sohl-Dickstein, P. Battaglino, and M. R. DeWeese (2011) New method for parameter estimation in probabilistic models: Minimum probability flow. Physical Review Letters, 107(22):220601. [https://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf pdf]

J. Zylberberg, J. T. Murphy, and M. R. DeWeese (2011) A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields. PLOS Computational Biology, 7(10):e1002250. [https://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf pdf]

J. Zylberberg, and M. R. DeWeese (2011) How should prey animals respond to uncertain threats? Frontiers in Computational Neuroscience, 5:20. doi: 10.3389/fncom.2011.00020. [https://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf pdf]

Tosic I., Olshausen B. A. and Culpepper B. J. (2011) Learning sparse representations of depth. IEEE Journal on Selected Topics in Signal Processing, Vol. 5, No 5, pp 941 - 952, 2011. [http://arxiv.org/abs/1011.6656 pdf]

Wang X, Sommer FT, Hirsch JA: Inhibitory circuits for visual processing in thalamus. Current Opinion in Neurobiology 21 (2011) 726-733

Wang X, Vaingankar V, Soto Sanchez C, Sommer FT, Hirsch JA (2011) Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing. Nature Neuroscience 14: 224-231

'''2010'''

Cadieu CF, Koepsell K (2010) Phase Coupling Estimation from Multivariate Phase Statistics. Neural Computation 22(12), pp. 3107 - 3126. [http://redwood.berkeley.edu/klab/papers/CadieuKoepsell_PCE_NeuralComp.pdf pdf]

Canolty RT, Ganguly K, Kennerley SW, Cadieu CF, Koepsell K, Wallis JD, Carmena JM (2010) Oscillatory phase coupling coordinates anatomically-dispersed cell assemblies. PNAS 107(40) 17356 - 17361. [http://dx.doi.org/10.1073/pnas.1008306107 journal] [http://redwood.berkeley.edu/klab/papers/PNAS-2010-Canolty-17356-61.pdf pdf][http://redwood.berkeley.edu/klab/papers/PNAS-2010-Canolty-17356-61_supp.pdf supplement]

Knoblauch A, Palm G, Sommer FT (2010) Memory capacities for synaptic and structural plasticity. Neural Computation, Volume 22 (2): 289-341 [http://redwood.berkeley.edu/fsommer/papers/knoblauchpalmsommer10.pdf pdf]

Koepsell K, Wang X, Hirsch JA, Sommer FT (2010) Exploring the function of neural oscillations in early sensory systems. Focused review in Frontiers in Neuroscience 4 (1): 53-61. [http://frontiersin.org/neuroscience/neuroscience/paper/10.3389/neuro.01/010.2010/ Frontiers in Neuroscience]

Lauritzen TZ, Ales JM, Wade AR (2010) The effects of visuospatial attention measured across visual cortex using source-imaged, steady-state EEG. J. of Vision 10(14)39: 1 - 17. [http://www.journalofvision.org/content/10/14/39.full.pdf+html pdf][http://www.journalofvision.org/content/10/14/39/suppl/DCSupplementaries supplement]

Tsao DY, Cadieu C, and Livingstone M (2010) Object Recognition: Physiological and Computational Insights. In Primate Neuroethology. Edited by M. Platt and A. Ghazanfar. Oxford University Press. 2010 (in press)

Wang X, Hirsch JA, Sommer FT (2010) Recoding of sensory information across the retinothalamic synapse. Journal of Neuroscience 30: 13567-13577

'''2009'''

Kanerva P (2009). Hyperdimensional Computing: An introduction to computing in distributed representation with high-dimensional random vectors. ''Cognitive Computation'' 1(2): 139-159 [http://www.springerlink.com/content/966151841g415165/ link] [http://redwood.berkeley.edu/pkanerva/papers/kanerva09-hyperdimensional.pdf pdf]

Koepsell K, Wang X, Vaingankar V, Wei Y, Wang Q, Rathbun DL, Usrey W, Hirsch J and Sommer FT (2009)
Retinal oscillations carry visual information to cortex. Front Syst Neurosci 3:4 [http://dx.doi.org/10.3389/neuro.06.004.2009 pdf]

Lauritzen TZ, D’Esposito M, Heeger D and Silver MA. (2009) Top-down flow of visual spatial attention signals from parietal to occipital cortex. Journal of Vision, 9(13):18, 1-14. [http://journalofvision.org/9/13/18/ link]

Ming, V. & Holt, L. (2009) Efficient coding in human auditory perception. J. Acoust. Soc. Am. 126.

Monaci G, Vandergheynst P, Sommer FT (2009) Learning bimodal structure in audio-visual data. IEEE Transactions on Neural Networks 20:1898-1910 [http://redwood.berkeley.edu/fsommer/papers/Monaci_etal_IEEE_NN09.pdf pdf]

Para LC, Beck JM, Bell AJ (2009) On the maximization of information flow between spiking neurons. Neural Computation, in press

'''2008'''

Koepsell K, Sommer FT (2008) Information transmission in oscillatory neural activity.
Biological Cybernetics 99:403–416 [http://dx.doi.org/10.1007/s00422-008-0273-6 abstract] [http://redwood.berkeley.edu/fsommer/papers/koepsellsommer08bicy.pdf pdf]

Rozell CJ, Johnson DH, Baraniuk RG, Olshausen BA (2008) Sparse Coding via Thresholding and Local Competition in Neural Circuits. Neural Computation, 20:2526-2563 [http://redwood.berkeley.edu/bruno/papers/garrigues_olshausen_nips2007.pdf pdf]

Teeters JL, Harris KD, Millman KJ, Olshausen BA, Sommer FT (2008) Data sharing for computational neuroscience. Neuroinformatics 6:47-55 [http://redwood.berkeley.edu/fsommer/papers/teetersetal08.pdf pdf]

Y. Yang, M. R. DeWeese, G. Otazu, and A. M. Zador. Millisecond-scale differences in neural activity in auditory cortex can drive decisions. Nature Neuroscience 11, 1262-1263 (2008). [https://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf pdf]

T. Hromadka, M. R. DeWeese, and A. M. Zador. Sparse representation of sounds in the unanesthetized auditory cortex. PLoS Biol. 6, 124-137 (2008). [https://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf pdf]

'''2007'''

Rehn M, Sommer FT (2007) A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. J. Comp. Neurosci. 22 (2): 135-146. [http://redwood.berkeley.edu/fsommer/papers/rehnsommer07jcns.pdf pdf]

Sommer FT (2007) Bunte Theorien für graue Zellen. [http://www.gehirn-und-geist.de/artikel/872207 Gehirn und Geist], Juni 70-76

Wang X, Wei Y, Vaingankar V, Wang Q, Koepsell K, Sommer FT, Hirsch JA (2007) Feedforward excitation and inhibition evoke dual modes of firing in the cat’s visual thalamus during naturalistic viewing. Neuron 55 (2007) 465-478. [http://redwood.berkeley.edu/fsommer/papers/wangetal07neuron.pdf pdf]
See also the preview about this paper: P. Reinagel: The inner life of bursts. Neuron 55: 339-341

'''2006'''

Bethge M (2006) Factorial coding of natural images: how effective are
linear models in removing higher-order dependencies?
''J. Opt. Soc. Am.'' A, 23(6): 1253-1268.

Rehn M, Sommer FT (2006) Storing and restoring visual input with collaborative rank coding and associative memory.
''Neurocomputing'' 69 (10-12) 1219-1223 [http://redwood.berkeley.edu/~fsommer/papers/rehnsommer06neurocomp.pdf pdf]

Sommer FT, Kanerva P (2006) Can neural models of cognition benefit from the advantages of connectionism?
''Behavoral and Brain Sciences'' 29 (1) 86-87 [http://redwood.berkeley.edu/~fsommer/papers/sommerkanerva05.pdf pdf]

M. R. DeWeese and A. M. Zador. Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex. J. Neuroscience 26(47), 12206-12218 (2006). [https://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf pdf]

'''2005'''

George D, Sommer FT (2005) Computing with inter-spike inverval codes
in networks of integrate and fire neurons. ''Neurocomputing'' 65-66, 414 -
420. [http://redwood.berkeley.edu/~fsommer/papers/georgesommer05.pdf pdf]

Johnson JS, Olshausen BA (2005) The recognition of partially visible
natural objects in the presence and absence of their occluders.
''Vision Research'', 45, 3262-3276. [http://redwood.berkeley.edu/bruno/papers/VR05-occlusion.pdf pdf]

Johnson JS, Olshausen BA (2005) The earliest EEG signatures of object
recognition in a cued-target task are postsensory. ''Journal of Vision'',
5, 299-312. [http://journalofvision.org/5/4/2/ link]

Martinez LM, Wang Q, Reid RC, Pillai C, Alonso J-M, Sommer FT,
Hirsch JA (2005) Receptive field structure varies with layer in the
primary visual cortex. ''Nature Neuroscience'' 8 , 372 - 379
[http://redwood.berkeley.edu/~fsommer/papers/martinezetal05.pdf pdf]

Olshausen BA, Field DJ (2005) How close are we to understanding V1?
''Neural Computation'', 17, 1665-1699. [http://redwood.berkeley.edu/bruno/papers/V1-article.pdf pdf]

Sommer FT, Wennekers T (2005) Synfire chains with conductance-based
neurons: internal timing and coordination with timed
input. ''Neurocomputing'' 65-66, 449 - 454.
[http://redwood.berkeley.edu/~fsommer/papers/sommerwennekers05.pdf pdf]

== Refereed Conference Proceedings ==

'''2015'''

'''2014'''

J. Sohl-Dickstein, M. Mudigonda, M.R. DeWeese. Hamiltonian Monte Carlo Without Detailed Balance. Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014). [https://redwood.berkeley.edu/w/images/2/2b/SohlDickstein_Mudigonda_DeWeese_Sampling_Without_Detailed_Ballance_preprint.pdf pdf]

'''2013'''

'''2012'''

'''2011'''

J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese (2011) Minimum Probability Flow Learning. Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA). [https://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf pdf]

G. Isely, C. Hillar, F. T. Sommer: Decyphering subsampled data: Adaptive compressive sampling as a principle of brain communication. Advances in Neural Information Processing Systems 23. Eds: J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta (2011) 910-918 [http://www.google.com/url?sa=t&source=web&cd=1&ved=0CBoQFjAA&url=http%3A%2F%2Fbooks.nips.cc%2Fpapers%2Ffiles%2Fnips23%2FNIPS2010_1099.pdf&ei=aPFvTdLvLZHGsAPZ-5i_Cw&usg=AFQjCNGKtk3i_t5iSyxpdMcqMoLFhKf8OA&sig2=dnZxP1kl2pav20u7wloNSw pdf]

'''2010'''

Canolty RT, Ganguly K, Kennerley SW, Cadieu CF, Koepsell K, Wallis JD, Carmena JM (2010)
Single-neuron spike timing depends on global brain dynamics. Computational and Systems
Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00264 [http://www.frontiersin.org/conferences/individual_abstract_listing.php?conferid=770&pap=3555&ind_abs=1&q=197 abstract]

M.A. Silver, A.N Landau, T.Z. Lauritzen, W Prinzmetal, L.C. Robertson. Isolating human brain functional connectivity associated with a specific cognitive process. Proceedings of SPIE Volume 7527 – In press.

Culpepper B.J., Olshausen B.A. Learning transport operators for image manifolds. Advances in Neural Information Processing Systems (NIPS), 22. (2010) Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams and A. Culotta. [http://www.cs.berkeley.edu/~bjc/culpepper-nips22.pdf pdf] [http://www.cs.berkeley.edu/~bjc/culpepper-nips22-supplementary.pdf supplementary materials]

'''2009'''

Huth A, Cadieu CF, Dale CL, Weber D, Pantazis D, Darvas F, Leahy R, Simpson GV, Koepsell K (2009) Detecting functional connectivity in networks of phase-coupled neural oscillators, Poster presentation, Computational and Systems Neuroscience. doi: 10.3389/conf.neuro.06.2009.03.258 [http://frontiersin.org/conferences/individual_abstract_listing.php?conferid=39&pap=1341 abstract]

Charles CF, Koepsell K (2009) A multivariate phase distribution and its estimation, Poster presentation, Computational and Systems Neuroscience. doi: 10.3389/conf.neuro.06.2009.03.260 [http://frontiersin.org/conferences/individual_abstract_listing.php?conferid=39&pap=1343 abstract]


Cadieu C.F., Olshausen B.A., (2009) Learning Transformational Invariants from Natural Movies. Advances in Neural Information Processing Systems (NIPS), 21:209-216, 2009. D. Koller and D. Schuurmans and Y. Bengio and L. Bottou, MIT Press, Cambridge, MA. [http://books.nips.cc/papers/files/nips21/NIPS2008_0200.pdf pdf] [Movies: Figure 2 [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure2.wmv wmv] / [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure2.mov mov] , 4a [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure4a.avi avi] / [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure4a.mov mov] , 4b [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure4b.avi avi] / [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure4b.mov mov] , 4c [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure4c.avi avi] / [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure4c.mov mov] , 4d [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure4d.avi avi] / [http://redwood.berkeley.edu/cadieu/pubs/videos/movie_TransInv_Figure4d.mov mov] ]

Olshausen, B., C. Cadieu, and D.K. Warland. (2009) Learning Real and Complex Overcomplete Representations from the Statistics of Natural Images, Proc. SPIE 7446, 74460S, 2009. [http://dx.doi.org/10.1117/12.825882 link]

'''2008'''

Garrigues P.J., El Ghaoui L., An Homotopy Algorithm for the Lasso with Online Observations. Advances in Neural Information Processing Systems 21 (NIPS 2008). [http://www.eecs.berkeley.edu/~garrigue/nips08_reclasso.pdf pdf]

Monaci G., Sommer F. T. and Vandergheynst P., Learning Sparse Generative Models of Audiovisual Signals, Proc. of European Conf. on Signal Processing (EUSIPCO08), 2008 [http://redwood.berkeley.edu/w/images/3/37/Eusipco08.pdf pdf]

'''2007'''

Garrigues P.J. Olshausen B.A. (2007) Learning Horizontal Connections in a Sparse Coding Model of Natural Images. To appear in Advances in Neural Information Processing Systems 20 (NIPS 2007) [http://www.eecs.berkeley.edu/~garrigue/nips07_horizontalconnections.pdf pdf]

Olshausen, B., C. Cadieu, J. Culpepper, and D.K. Warland. (2007) Bilinear Models of Natural Images, Proc. SPIE Int. Soc. Opt. Eng. 6492, 649206, February 2007. [http://redwood.berkeley.edu/cadieu/pubs/bilinear-SPIE07.pdf pdf]

'''2005'''

Bell A.J., Parra L.C. (2005) Maximising Sensitivity in a Spiking Network, ''Advances in Neural Information Processing Systems 17'', Saul L.K. and Weiss Y. and Bottou L., MIT Press, Cambridge, MA
[http://newton.bme.columbia.edu/~lparra/publish/nips04-BellParra.pdf pdf]

== Technical Reports ==
Cadieu CF, Koepsell K (2010) Modeling Image Structure with Factorized Phase-Coupled Boltzmann Machines. November 2010. [http://arxiv.org/abs/1011.4058 arXiv:1011.4058v1] [cs.CV]. [http://arxiv.org/pdf/1011.4058 pdf]

Kanerva P (2010). What we mean when we say "What's the Dollar of Mexico?": Prototypes and mapping in concept space. Report FS-10-08-006, AAAI Fall Symposium on Quantum Informatics for Cognitive, Social, and Semantic Processes. [http://www.aaai.org/Press/press.php link]. [http://redwood.berkeley.edu/pkanerva/papers/kanerva10-dollarofmexico.pdf pdf]

Sohl-Dickstein J, Wang CM, Olshausen BA (2010) An Unsupervised Algorithm For Learning Lie Group Transformations. [http://arxiv.org/abs/1001.1027 pdf]

Cadieu CF, Koepsell K (2009) Phase Coupling Estimation from Multivariate Phase Statistics. June 2009. [http://arxiv.org/abs/0906.3844v1 arXiv:0906.3844v1] [nlin.AO]. [http://arxiv.org/pdf/0906.3844v1 pdf]

W. K. Coulter, C. J. Hillar, F. T. Sommer (2009) Adaptive compressed sensing - a new class of self-organizing coding models for neuroscience. arXiv.org > q-bio > arXiv:0906.1202 [http://redwood.berkeley.edu/fsommer/papers/0906.1202v1.pdf pdf]

Cadieu C, Koepsell K (2008) A Multivariate Phase Distribution and its Estimation. September 2008. [http://arxiv.org/abs/0809.4291v2 arXiv:0809.4291v2] [q-bio.NC]. [http://arxiv.org/pdf/0809.4291v2 pdf]

M. R. DeWeese. Whole-Cell Recording In Vivo. Chapter 6 in Current Protocols in Neuroscience. John Wiley & Sons, Inc., pp. 6.22.1-15 (2007). [https://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf pdf]

== Talks and Posters ==

'''2010'''

Koepsell K (2010) Collective computation with neural assemblies -- from connectivity to network dynamics and back. Invited talk, Center for Mind and Brain, UC Davis, CA.

Koepsell K (2010) The role of oscillations for cortical communications. Invited talk, Theory Group, Los Alamos National Lab, Los Alamos, NM.

Rokem A, Trumpis M, Perez F, Ivanov P, Koepsell K, Blanche T, Fegen D, D’Esposito M (2010) Nitime: an open-source package for time-series analysis of neuroscience data. Poster presentation, Human Brain Mapping Annual Meeting, Barcelona, Spain.

Koepsell K (2010) Detecting functional connectivity in networks of phase-coupled neural oscillators. Invited talk, Workshop on Multi-Scale Complex Dynamics in the Brain at CSYNE 2010.

'''2009'''

Cadieu, C., L. Secundo, E. Chang, B.J. Culpepper, N.M. Barbaro, B.A. Olshausen, R.T. Knight. (2009) Sparse Space-Time Decompositions of ECoG signals. Program No. 894.9/HH7. Chicago, IL: Society for Neuroscience, 2009.

Koepsell K (2009) Phase coupling estimation from multivariate phase statistics. Invited talk, ''Smith-Kettlewell Eye Research Institute'', San Francisco, California.

Koepsell K (2009) Phase coupling estimation in coupled oscillator systems. Invited talk, ''Mathematical Biology Seminar'', UC Davis.

Cadieu CF, Koepsell K (2009) A multivariate phase distribution and its estimation. ''Computational and Systems Neuroscience'', Salt Lake City, UT. Frontiers in Systems Neuroscience. Conference Abstract: Computational and systems neuroscience. doi: 10.3389/conf.neuro.06.2009.03.260

Huth A, Cadieu CF, Dale CL, Weber D, Pantazis D, Darvas F, Leahy R, Simpson GV, Koepsell K (2009) Detecting functional connectivity in networks of phase-coupled neural oscillators. ''Computational and Systems Neuroscience'', Salt Lake City, UT. Frontiers in Systems Neuroscience. Conference Abstract: Computational and systems neuroscience. doi: 10.3389/conf.neuro.06.2009.03.258

T.Z. Lauritzen and A. Wade. (2009) Different cortical areas are modulated in different ways by spatial attention in human visual cortex. Optical Society of America, Fall vision meeting. September 24-26, 2009, Seattle, Washington, and Journal of Vision, 9(14):44, 44a. [http://journalofvision.org/9/14/44/ link]

T.Z. Lauritzen and A. Wade. (2009) Spatial attention modulates steady state visually-evoked potentials in human visual cortex by a multiplicative gain function. Annual meeting of the Organization for Human Brain Mapping, San Francisco, CA, June 18-23, 2009.

T.Z. Lauritzen and A. Wade. (2009) Spatial attention modulates steady state VEPs in retinotopic human visual cortex. COSYNE Meeting, Salt Lake City and Snowbird, Utah, February 26 - March 3, 2009.

'''2008'''

Blanche TJ, Koepsell K, Swindale N, Olshausen BA (2008) Predicting response variability in the
primary visual cortex. ''Computational and Systems Neuroscience'', Salt
Lake City, UT.

Canolty RT, Soltani M, Koepsell K, Cadieu C, Dalal SS, Edwards E, Nagarajan SS, Kirsch HE, Barbaro NM and Knight RT (2008). Auditory target detection activates frontal and parietal cortices: Evidence from high gamma power and low-frequency phase coherence in the subdural electrocorticogram. Frontiers in Human Neuroscience. Conference Abstract: 10th International Conference on Cognitive Neuroscience. doi: 10.3389/conf.neuro.09.2009.01.119

Cadieu, C., B. Olshausen. (2008) Learning Transformational Invariants from Time-Varying Natural Images. Computational and Systems Neuroscience (Cosyne), Salt Lake City, March 2008. [http://redwood.berkeley.edu/cadieu/pubs/CadieuOlshausen2008_Cosyne-abstract.pdf abstract] [http://redwood.berkeley.edu/cadieu/pubs/videos/Cadieu_Cosyne08.mov talk]

Koepsell K (2008) Spike timing in the context of network dynamics from retina to cortex.
Conference talk, ''Computational and Systems Neuroscience'', Salt Lake City, UT.

Koepsell K, Blanche TJ, Swindale N, Olshausen BA (2008) Modeling the inﬂuence of local
network activity on neuron spiking responses in primary visual cortex.
''Computational and Systems Neuroscience'', Salt Lake City, UT.

T.Z. Lauritzen, M. D’Esposito, D. Heeger and M.A. Silver. (2008) Functional networks underlying human top-down visual spatial attention. Annual Cognitive Neuroscience Society Meeting. April 12-15, 2008, San Francisco, California.

T.Z. Lauritzen, M. D’Esposito, D. Heeger and M.A. Silver. (2008) Human visual attention networks revealed by fMRI coherency analysis. COSYNE Meeting, Salt Lake City and Snowbird, Utah, February 28 - March 4, 2008.

'''2007'''

Blanche TJ (2007) The influence of cortical dynamics on spike timing precision in cat V1. ''Smith-Kettlewell Eye Research Institute'', San Francisco, California.

Cadieu, C., B. Olshausen. (2007) Learning Invariant and Variant Components of Time Varying Natural Images Using a Sparse, Multiplicative Model. Computational and Systems Neuroscience (Cosyne) Salt Lake City, March 2007, & Vision Sciences Society, Sarasota May 2007. [http://redwood.berkeley.edu/cadieu/pubs/CadieuOlshausen_Cosyne_2007.pdf abstract]

Koepsell K (2007) The hidden clock in LGN -- is phase coding employed in early vision?.
''Göttingen Neurobiology Conference'', Göttingen, Germany

Garrigues PJ, Olshausen BA (2007) Learning Horizontal Connections from the Statistics of Natural Images. ''Computational and Systems Neuroscience'', Salt Lake City, Utah.

Blanche TJ (2007) How active is the cortex? ''COSYNE [http://www.cosyne.org/How_active_is_the_cortex%3F workshop]'', The Canyons, Utah.

Blanche TJ, Koepsell K (2007) Spike timing precision and the influence of cortical dynamics. ''Grand Challenges in Neural Computation'', Santa Fe, New Mexico.

T.Z. Lauritzen, M. D’Esposito, D. Heeger and M.A. Silver. (2007) Functional networks underlying top-down visual spatial attention in the human brain. Society for neuroscience annual meeting, 2007, abstract 423.9.

Lauritzen TZ, Shenhav, Silver MA (2007) fMRI coherency analysis reveals feedforward progression of visual responses in human early visual cortex ''OSA fall vision meeting'', Berkeley, CA.

Lauritzen TZ (2007) Functional networks underlying top-down visual spatial attention in the human brain ''IMM, Danish Technical University, Lyngby, Denmark''.

Lauritzen TZ (2007) Neural mechanisms of sustained visual spatial attention ''ENS, Paris, France''.

'''2006'''

Blanche TJ, Freiwald WA, Swindale NV (2006) Neural sparseness in cat and monkey visual cortex studied with silicon polytrode arrays. ''Society for Neuroscience''.

Koepsell K, Wang X, Wei Y, Wang Q, Vaingankar V, Hirsch JA, Sommer FT (2006)
Retinal oscillations carry visual information to cortex. ''Computational and Systems Neuroscience'', Salt Lake City, Utah.

Koepsell K, Wang X, Wei Y, Wang Q, Vaingankar V, Hirsch JA, Sommer FT (2006)
Two channels for visual information to travel from thalamus to cortex. ''Society for Neuroscience''.

Lauritzen TZ (2006) Attention modulation mediated through correlations in the local field potential ''Annual cognitive neuroscience soc meeting'', San Francisco, CA.

'''2005'''

Koepsell K, Wang X, Wei Y, Wang Q, Vaingankar V, Hirsch JA, Sommer FT (2005)
Ongoing retinal activity explains variability of thalamic responses. ''Society for Neuroscience''.

Lauritzen TZ (2005) Correlations in the local field potential yield non-linear neural response changes ''Society for Neuroscience'', Abstract 274.25.

Lauritzen TZ (2005) Contribution of physiological noise to dendritic non-linearities ''COSYNE'', Salt Lake City, Utah.

== Redwood Neuroscience Institute ==

An incomplete list of publications from the Redwood Neuroscience Institute (2002-2005) is available [http://www.rni.org/pubs.html here] (will be updated soon).