cosyne computational and systems neuroscience 2006 · of hippocampal place cells ricardo...

COSYNE Computational and Systems Neuroscience

Main Meeting

March 5-8 Salt Lake City, Utah

Workshops March 9-10

Park City, Utah

Cosyne Executive Board

Tony Zador (CSHL)

Alex Pouget (Rochester) Zach Mainen (CSHL)

Mike Shadlen (Univ. Washington) Carlos Brody (CSHL)

2006 Meeting Organization

Program Chair: Zach Mainen (CSHL) Workshop Chair: Peter Latham (Gatsby) Publicity Chair: Adam Kepecs (CSHL)

Program Committee: Loren Frank (UCSF)

Michael Häusser (UCL) Adam Kepecs (CSHL)

Flip Sabes (USCF) Eero Simoncelli (NYU)

Stefan Treue (GPC, Gottigen)

Table of Contents

Schedule overview ................................................................................................................................. p. 3 Main meeting program Sunday, March 5.............................................................................................................................. p. 4 Monday, March 6 ............................................................................................................................. p. 5 Tuesday, March 7 ............................................................................................................................ p. 7 Wednesday, March 8....................................................................................................................... p. 9 Poster listings, Session I................................................................................................................p. 11 Poster listings, Session II...............................................................................................................p. 16 Author Index (Main meeting only) .................................................................................................p. 21 Workshop program Thursday, March 8 .........................................................................................................................p. 28 Friday, March 9 ..............................................................................................................................p. 29 Detailed workshop program..........................................................................................................p. 30

Notes on talks

Invited talks [bold]: Selected by the Executive Board; 40 min including questions Contributed talks: Selected by the Program Committee; 20 min including questions

Food included with registration

Main Meeting – Marriott: Sunday 3/5 Evening Reception (with cash bar) Monday 3/6 Continental Breakfast, Morning Beverage Break, Afternoon Beverage Break Tuesday 3/7 Continental Breakfast, Morning Beverage Break, Afternoon Beverage Break Wednesday 3/8 Continental Breakfast, Morning Beverage Break

Workshops – The Canyons: Thursday 3/9 Hot Breakfast Buffet, Morning Beverage Break, Afternoon Beverage Break Friday 3/10 Hot Breakfast Buffet, Morning Beverage Break, Afternoon Beverage Break, Dinner Buffet (with cash bar)

Additional Credits

Organizational support: Christina Laycock and the Univ. Rochester Conference & Events Office Web site support: Shulamit Avraham

Schedule Overview

Cosyne Main Meeting Marriott–Downtown, Salt Lake City, Utah

Sunday, March 5 Location

7:30 – 8:45PM Reception (Hot and cold hors d'oeuvres & cash bar) Salon F 8:45 – 9:00 Welcome and announcements Salons D–E 9:00 – 10:00 Keynote: Anthony Movshon (NYU) What MT does Salons D–E

Monday, March 6

7:00 – 8:00 AM Continental Breakfast Foyer 8:00 – 11:50 Slide Session 1 ` Salons D–E 1:30 – 5:15 PM Slide Session 2 Salons D–E 8:00 – 11:00 Poster Session I (#39-142) Salons F–J

Tuesday, March 7

7:00 – 8:00 AM Continental Breakfast Foyer 8:00 – 11:45 Slide Session 3 Salons D–E 1:30 – 5:15 PM Slide Session 4 Salons D–E 8:00 – 11:00 Poster Session II (#143-245) Salons F–J

Wednesday, March 8

7:00 – 8:00 AM Continental Breakfast Foyer 8:00 – 11:50 Slide Session 5 Salons D–E 1:30 – 4:10 PM Slide Session 6 Salons D–E 5:00 – 5:30 Buses board for The Canyons 100 South Entrance

Cosyne Workshops The Canyons, Park City, Utah

Thursday, March 9 Location 7:30 – 8:30 AM Full Breakfast Kokopelli Parlor II 8:30 – 11:30 Workshops Parlor rooms 4:30 – 7:30 PM Workshops continue Parlor rooms Friday, March 10 7:30 – 8:30 AM Full Breakfast Kokopelli Parlor II 8:30 – 11:30 Workshops Parlor rooms 4:30 – 7:30 PM Workshops continue Parlor rooms 8:00 – 11:30 Banquet Kokopelli Grand Ballroom

Cosyne Main Meeting

Marriott–Downtown Salt Lake City, Utah

Sunday, March 5

7:30 – 8:45 PM Reception Salon F Hot and cold hors d'oeuvres & cash bar

Plenary Session 8:45 – 10:00 PM Salons D–E

8:45 Welcome and announcements 9:00 Keynote: Anthony Movshon (NYU) What MT does

Monday, March 6

7:00 – 8:00 AM Continental breakfast Foyer

Slide Session 1 8:00 – 11:45 AM Salons D–E

Cost of a spike 8:00 Peter Lennie (NYU) How busy is cortex? 8:40 David Attwell (UCL) Matching energy supply to neural computation in the cerebellum 9:20 Chip Levy (Univ. Virginia) The metabolic energy cost of action potential velocity 9:40 – 10:10 Beverage break Foyer

Making spaces 10:10 Neil Burgess (UCL) The hippocampus, space and memory 10:50 Hugh Blair, Kechen Zhang (UCLA) Moiré interference between grid cells: A mechanism for representing space at multiple scales 11:10 Maté Lengyel, Peter Dayan (Gatsby) Firing rates and times in the hippocampus: What are they good for? 11:30 Poster previews

#55 U. Eden et al. Analysis of oscillatory spiking in the subthalamic nucleus of Parkinson’s patients using point process models #91 M. Belova et al. The time course of reward and punishment prediction error signals in the primate amygdala accounts for learning #112 C. Eliasmith et al. Biologically realistic neural inhibition in arbitrary neural circuits

11:45 Lunch break

Slide Session 2 1:30 – 5:15 PM Salons D–E

Learning from spike times 1:30 Karel Svoboda (CSHL) Imaging synaptic plasticity in vivo 2:10 Ithai Rabinowitch, Idan Segev (Hebrew Univ.) Homeostatic synaptic plasticity in dendrites: is it local or global? 2:30 Haim Sompolinsky (Hebrew Univ.) Deciding in time: Learning spike-timing based categorization 3:10 – 3:40 Beverage break Foyer 3:40 Surya Ganguli (UCSF) Learning and memory in an exactly solvable stochastic spiking network

4:00 Robert Froemke, Michael Merzenich, Christoph Schreiner (UCSF) Synaptic logic of cortical neuromodulation and plasticity 4:20 Massimo Scanziani (UCSD) Dynamics of feedback inhibitory circuits 5:00 Poster previews

#121 U. Beierholm et al. Do within modality and cross-modality sensory integration follow the same rules? #140 R. Liu, C. Schreiner. An information theoretic approach to detecting and discriminating mouse communication sounds #158 K. Denning, P. Reinagel. Contrast gain control in the LGN optimizes information transfer, but may not require any dynamic adaptation process

5:15 Dinner break

Poster Session I 8:00 – 11:00 PM Salons F–J Posters #39-142

Tuesday, March 7

Movements under control 8:00 Steven Lisberger (UCSF) Origins of motor noise 8:40 Michele Rucci, Ramon Iovin, Gaelle Desbordes (BU) Fixational eye movements and the representation of natural scenes 9:00 Emo Todorov (UCSD) Optimality principles in sensorimotor control 9:40 – 10:10 Beverage break Foyer 10:10 Vidhya Navalpakkam, Laurent Itti (USC) A theory of optimal feature selection during visual search 10:30 Konrad Kording, Josh Tenenbaum, Reza Shadmehr (MIT) Motor adaptation as Bayesian inference

Getting it together 10:50 Dora Angelaki (Wash U) Self-motion perception: Multisensory integration in extrastriate visual cortex 11:30 Rama Natarajan, Peter Dayan, Quentin Huys, Richard Zemel (Univ. Toronto) Population codes for dynamic cue combination 11:50 Lunch break

1:30 Kenneth Whang (NSF) Funding for computational neuroscience

Learning to sing 1:50 Michale Fee (MIT) A dedicated circuit drives vocal exploration in juvenile songbirds 2:30 Sebastian Seung (MIT) Theory of gradient learning with “empiric synapses” 3:10 – 3:40 Beverage break Foyer 3:40 Allison Doupe (UCSF) Social context and neural coding in a basal ganglia-forebrain circuit essential for vocal plasticity

Neurons get together 4:20 E.J. Chichilnisky, Eric Frechette, Alexander Sher, Matthew Grivich, Dumitru Petrusca, Alan Litke (Salk Inst.) Ensemble coding of visual motion in primate retina and its readout in the brain 4:40 Andreas Tolias, Alexander Ecker, Georgios Keliris, Thanos Siapas, Stelios Smirnakis, Nikos Logothetis (Max-Plank Inst., Tübingen) Structure of interneuronal correlations in the primary visual cortex of the rhesus macaque 5:00 Poster previews

#174 Y. Sakai, T. Fukai State-dependent matching law in stochastic gradient ascent #206 D. Nykamp Inferring causal subnetworks using point process models #207 N. Parush et al. An algebraic approach to the analysis of network functional connectivity: Application on data from the basal ganglia

5:15 Dinner break

Poster Session II 8:00 – 11:00 PM Salons F–J Posters #143–245

Wednesday, March 8

Circuits for choices 8:00 Jeff Schall (Vanderbilt) Interactive race model of countermanding saccades 8:40 Bruno Averbeck, Daeyeol Lee (U. Rochester) Prefrontal neural correlates of errors in a sequential decision making task 9:00 John Maunsell (Baylor) Thresholds for detecting electrical microstimulation in cerebral cortex 9:40 Bijan Pesaran, Matthew Nelson, Richard Andersen (Caltech) Free choice increases synaptic interactions between frontal and parietal cortex 10:00 – 10:30 Beverage break Foyer

Views on optimality 10:30 Tatyana Sharpee, William Bialek (UCSF) Optimal neural decision boundaries for maximal information transmission 10:50 Andreas Herz (Berlin) Testing Barlow's ‘Efficient Coding Hypothesis’: Are sensory neurons really matched to natural stimuli? 11:10 Marcus Raichle (Wash. U.) Spontaneous activity and the brain's dark energy 11:50 Lunch break

Seeing by hearing 1:30 Cynthia Moss (Univ. Maryland) Steering by hearing in echolocating bats 2:10 Hiroshi Riquimaroux, Shizuko Hiryu, Yoshiaki Watanabe (Doshisha Univ.) The strategy for echolocating bats to shift attention from one target to another measured by a telemetry microphone system

Let the brain decide 2:30 Peter Dayan (Gatsby) Phasic norepinephrine and neural interrupts

3:10 Kenway Louie, Paul Glimcher (NYU) Temporal discounting activity in parietal neurons during intertemporal choice 3:30 Leslie Ungerleider (NIH) Mechanisms for decision-making in the human brain" 4:10 End

5:00 PM Buses board for The Canyons, Park City Marriott, 100 South Entrance 5:30 Last bus departs

Poster presentations, Session I

Cellular/synaptic 39. Prediction: Linear and Nonlinear Synaptic Integration zones in Basal Dendrites of Neocortical Pyramidal Cells Bardia F Behabadi1, Alon Polsky2, Jackie Schiller2, Bartlett W Mel1 1University of Southern California 2Technion Medical School, Haifa, Israel 40. Response properties and synchronization of dendritic neurons: theory and experiment Joshua A Goldberg, Chris Deister, Charles J Wilson University of Texas at San Antonio 41. Bayes points the way: an optimal strategy for growth cone chemotaxis Duncan Mortimer1, Peter Dayan2, Kevin Burrage1, Geoffrey J Goodhill1 1University of Queensland 2Gatsby Computational Neuroscience Unit 42. Studies of dendritic spike initiation and propagation in CA1 pyramidal cell models Yael Katz, William L Kath, Nelson Spruston Northwestern University

Coding/computation 43. Problems in Learning Efficient Nonlinear Representations: examples with quadratic codes relating to spike-triggered covariance analysis Mark V. Albert, David J. Field Cornell University 44. Effects of variable inhibition on spike timing precision in the olfactory bulb Maxime Ambard, Dominique Martinez LORIA (France) 45. Population Coding in V1 Charles H Anderson1, Gregory C DeAngelis1, J A Movshon2 1Washington Univ. School of Medicine 2New York University 46. Context dependence of neural responses in rat primary auditory cortex Hiroki Asari1, Hysell Oviedo2, Anthony M Zador2 1Watson School of Biological Sciences / Cold Spring Harbor Laboratory 2Cold Spring Harbor Laboratory 47. Bayesian inference with probabilistic population codes: Theory Jeffrey M Beck1, Weiji Ma1, Alexandre Pouget1, Peter Latham2 1University of Rochester 2Gatsby Computational Neuroscience Unit 48. Adaptation and the role of temporal precision in the visual code Daniel A Butts1, Chong Weng2, Jianzhong Jin2, Chun-I Yeh2,

Nick A Lesica1, Jose-Manuel Alonso2, Garrett B Stanley1 1Harvard University 2SUNY - State College of Optometry 49. Using a population reference for stimulus onset time in first spike latency coding Steven M Chase, Eric D Young Johns Hopkins University 50. A feed-forward model of spatial and directional selectivity of hippocampal place cells Ricardo Chavarriaga, Denis Sheynikhovich, Thomas Strosslin, Wulfram Gerstner Ecole Polytechnique Fédérale de Lausanne (EPFL), School of Computer and Communication Sciences, and Brain Mind Institute, 1015 Lausanne, Switzerland 51. Bayesian sampling methods for the analysis of electrophysiological data Beau D Cronin, Konrad P Kording MIT 52. The Effect of the Static Nonlinearity on the Efficient Coding of the Visual input. Mohammad Dastjerdi, Dawei W Dong Center for Complex Systems & Brain Sciences, Florida Atlantic University 53. Population coding of natural images with sensory and channel noise Eizaburo Doi, Michael S Lewicki Carnegie Mellon University 54. The dynamic receptive fields of the lateral geniculate nucleus (lgn) during free-viewing natural time-varying images Dawei W Dong1, Theodore G Weyand2, Martin Usrey3 1Center for Complex Systems and Brain Sciences, Florida Atlantic University 2Department of Cell Biology and Anatomy, Louisiana State University Health Science Center 3 Center for Neuroscience, University of California, Davis, California 55. Analysis of oscillatory spiking in the subthalamic nucleus of Parkinson’s patients using point process models Uri T Eden, Ramin Amirnovin, Emery N Brown, Emad N Eskandar Massachusetts General Hospital 56. A model of multiplicative auditory responses in the midbrain of the barn owl Brian J. Fischer1, Charles H. Anderson2 1California Institute of Technology 2Washington University School of Medicine 57. Selectivity, sparseness and information transmission in the inferior temporal visual cortex Leonardo Franco1, Edmund T Rolls2, Jose M Jerez1, Nick Aggelopoulos2 1University of Malaga 2University of Oxford 58. Structure of the Primate Cone Mosaic and the Statistics of Color in Natural Images Patrick Garrigan, Charles Ratliff, Jennifer M. Klein, Peter

Sterling, David H. Brainard, Vijay Balasubramanian University of Pennsylvania 59. Neuroinformatic Resources for Single- and Multi-Neuron Spike Train Analysis David H Goldberg, Jonathan D. Victor, Daniel Gardner Weill Medical College of Cornell University 60. Spike-timing effects in reverse correlation analyses Tim Gollisch Harvard University 61. A Novel Measure from Machine Learning to Describe Neural Responses Arnulf B.A. Graf, Adam Kohn Center for Neural Science, New York University 62. Modelling Adaptive Mechanisms for Motion Processing in the Macaque Visual Cortex Nicolas Heess, Wyeth Bair University Laboratory of Physiology; University of Oxford 63. Single neuron computation: from dynamical system to feature detector Sungho Hong1, Blaise Aguera y Arcas2, Adrienne L Fairhall1 1Department of Physiology and Biophysics, University of Washington 2Program in Applied Mathematics and Computation, Princeton University 64. Burst Temporal Coding by the Retina Toshiyuki Ishii, Toshihiko Hosoya RIKEN Brain Science Institute 65. Simultaneous electrophysiology and two-photon imaging of olfactory projection neurons in intact fruit flies Vivek Jayaraman, Gilles J. Laurent California Institute of Technology 66. Is the Cortex a Digital Computer? Dana H Ballard, Janneke FM Jehee University of Rochester 67. Representation of time and states in prefrontal cortex and striatum Dezhe Z Jin1, Naotaka Fujii2, Ann M Graybiel3 1Department of Physics, The Pennsylvania State University 2Brain Science Institute, RIKEN, Japan 3Department of Brain and Cognitive Sciences and the McGovern Institute for Brain Research, Massachusetts Institute of Technology 68. Transmission of rapidly changing signals through a population of noisy integrate-and-fire neurons Peyman Khorsand, Frances S Chance University of California, Irvine 69. Information Traffic on a Neural Cable Kristin Koch1, Ronen Segev2, Judith McLean1, Vijay

Balasubramanian1, Michael Freed1, Michael J Berry2, Peter Sterling1 1University of Pennsylvania 2Princeton University

70. Selectivity of local field potentials and spikes to the visual stimuli in the human medial temporal lobe Alexander Kraskov1, Rodrigo Quian Quiroga2, Itzhak Fried3, Christof Koch1 1Division of Biology, Caltech 2Department of Engineering, University of Leicester, UK 3Div. of Neurosurgery and Semel Institute for Neuroscience and Human Behavior UCLA, Functional Neurosurgery Unit, Tel-Aviv Medical Center and Sackler Faculty of Medicine, Tel-Aviv University 71. Common-input models for multiple neural spike-train data Liam Paninski, Jayant E Kulkarni Columbia University 72. Propagation of Synfire Activity in Locally Connected Networks with Conductance-based Synapses Arvind Kumar1, Stefan Rotter2, Ad Aertsen3 1Neurobiology and Biophysics, Insti. of Biology III, Albert-Ludwigs University Freiburg, Germany 2Theory and Data Analysis, IGPP, Freiburg and Bernstein Center for Computational Neuroscience Freiburg, Germany 3Neurobiology and Biophysics, Insti. of Biology III, Albert-Ludwigs University Freiburg, Germany, Bernstein Center for Computational Neuroscience. Freiburg, Germany 73. Requiem for the spike? Peter E. Latham1, Arnd Roth2, Michael Hausser2, Mickey London2 1Gatsby Computational Neuroscience Unit, UCL 2Wolfson Institute for Biomedical Research and Department of Physiology, UCL 74. Neural Diversity and Ensemble Encoding Aurel A. Lazar Columbia University 75. Bayesian inference with probabilistic population codes: Simulations in a network of conductance-based integrate-and-fire neurons Wei Ji Ma1, Jeffrey M Beck1, Peter E Latham2, Alexandre Pouget1 1University of Rochester 2University College London 76. The representation of interaural time differences in human cortex David McAlpine, Adenike O Deane-Pratt University College London 77. Reconstruction of speech stimuli from population of neuronal responses in primary auditory cortex Nima Mesgarani, Stephen David, Shihab Shamma University of Maryland College Park 78. Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments Keiji Miura1, Masato Okada2, Shun-ichi Amari3 1Kyoto University / JST PRESTO 2University of Tokyo / JST PRESTO / RIKEN BSI 3RIKEN BSI

Decisions/cognition 79. Formation of attractor representations of abstract rules in cortical networks

Emanuele Curti1, Xiao-Jing Wang2, Stefano Fusi3 1Columbia University, New York, NY 2Brandeis University, Waltham, MA 3Columbia Univ. New York, NY and ETH, Zurich, Switzerland 80. "Stochastic Multi-stability in Neural Decision-Making Systems" Gustavo Deco1, Alexander Roxin2, Ralph Andrzejak2, Daniel Martí2 1ICREA/Universitat Pompeu Fabra 2Universitat Pompeu Fabra 81. Orbitofrontal cortex responses during acquisition of novel stimulus-response associations Claudia E Feierstein, Zachary F Mainen Watson School of Biological Sciences, Cold Spring Harbor Laboratory 82. Malignant Evaluation: Reinforcement Learning, Neuromodulation and Depression Quentin JM Huys, Peter Dayan Gatsby Computational Neuroscience Unit, University College London 83. Activity in the dorsolateral prefrontal cortex of macaques during an inter-temporal choice task Jaewon Hwang1, Daeyeol Lee2 1Brain & Cognitive Sciences, University of Rochester 2Center for Visual Science, University of Rochester 84. Behavioral impact and neural representation of uncertainty in olfactory decision-making in rats Adam Kepecs, Naoshige Uchida, Zachary F Mainen CSHL 85. Adaptive reinforcement learning for motivated behavior Giancarlo La Camera, Zeng Liu, Dominique L Pritchett, Barry J Richmond NIMH 86. A neural network model of the Eriksen task Yuan Liu1, Philip J Holmes2 1Department of Physics, Princeton University 2Department of Mechanical and Aerospace Engineering, Princeton University 87. Synaptic plasticity and decision making: a neural model for operant matching Yonatan Loewenstein, Sebastian H Seung Howard Hughes Medical Institute and Massachusetts Institute of Technology 88. Phase space embedding of neural activities in the prefrontal cortex during a two-interval discrimination task Christian K Machens1, Ranulfo Romo2, Carlos D Brody1 1Cold Spring Harbor Laboratory 2UNAM Mexico 89. Performance following predicted reward postponement is well explained by temporal discounting. Takafumi Minamimoto, Giancarlo LaCamera, Barry J Richmond NIMH, NIH

Learning/plasticity

90. Temporally displaced STDP: Synaptic Competition and Stability Baktash Babadi, Majid Arabgol School of Cognitive Sceinces (SCS),IPM 91. The time course of reward and punishment prediction error signals in the primate amygdala accounts for learning Marina A Belova*, Joseph J Paton*, Daniel Salzman Columbia University 92. Plasticity of reverberatory activity in a prototypic hebbian cell assembly Pak-Ming Lau, Guo-Qiang Bi University of Pittsburgh School of Medicine 93. A principle for learning egocentric-allocentric transformations. Patrick A Byrne, Suzanna Becker McMaster University 94. Neural Correlates of Difference in Strategy of Adaptation to Force Perturbations Xinying Cai, Yury P Shimansky, Jiping He Arizona State University 95. Transitions in a bistable model of the calcium/calmodulin-dependent protein kinase-phosphatase system in response to STDP protocols Michael Graupner, Nicolas Brunel Laboratoire de Neurophysique et Physiologie, CNRS UMR 8119, Université René Descartes - Paris 5, Paris, France 96. The Tempotron: A Neuron that Learns Spike-Timing-Based Decisions Robert Guetig, Haim Sompolinsky Hebrew University of Jerusalem 97. A computational model for self-organized learning of sparse temporal sequences in zebra finch HVC Joseph K Jun, Dezhe Z Jin Penn State University 98. Basis for Training-Induced Plasticity of Auditory Localization in Adult Mammals Andrew J King, Oliver Kacelnik, Fernando R Nodal, Carl H Parsons Department of Physiology, Anatomy and Genetics, University of Oxford 99. An integrate-and-fire model of temporal context specific episodic encoding and retrieval in the hippocampal formation Randal A Koene, Michael E Hasselmo Boston University Center for Memory and Brain 100. Learning of Representations in a Canonical Model of Cortical Columns Jörg Lücke Gatsby Computational Neuroscience Unit, UCL, UK 101. Conserving mean activity through adaptive inhibition leads to temporal sharpening when combined with Hebbian enhancement of excitatory connections

Samat B Moldakarimov1, James L McClelland2, Bard G Ermentrout1 1University of Pittsburgh 2Carnegie Mellon Unibersity

Motor/sensorimotor 102. Adaptive control and the flow of information in the brain Mohamed N Abdelghani1, Timothy P Lillicrap2, Douglas B Tweed3 1University of Toronto 2Queens University 3Univeristy of Toronto 103. Learning to learn: motor adaptive strategies change with environmental experience Michael S. Fine, Jordan A. Taylor, Kurt A. Thoroughman Washington University 104. Learning without synaptic change: a mechanism for sensorimotor control Kristen P Fortney, Douglas B Tweed University of Toronto 105. Electrotaxis of C. elegans in fixed and time-varying fields Christopher V Gabel, Aravinthan Samuel Department of Physics. Harvard University 106. ECHOLOCATING BATS USE A PREY INTERCEPT STRATEGY THAT IS TIME-OPTIMAL IN A LOCAL, PIECE-WISE LINEAR SENSE Kaushik Ghose1, Timothy K Horiuchi2, P. S. Krishnaprasad2, Cynthia F Moss3 1Dept. Psychology, Neuroscience and Cognitive Science Program, University of Maryland, College Park 2Dept. Electrical and Computer Engineering, Neuroscience and Cognitive Science Program, Institute for Systems Research, University of Maryland, College Park 3Dept. Psychology, Neuroscience and Cognitive Science Program, Institute for Systems Research, University of Maryland, College Park 107. Implications of threshold nonlinearities on mechanisms underlying persistent neural activity in a bilateral neural integrator Itsaso Olasagasti1, Emre Aksay2, Guy Major2, David W Tank2, Mark S Goldman1 1Wellesley College 2Princeton University 108. A unified optimal control treatment of reaching and tracking Dongsung Huh, Emanuel Todorov UCSD 109. Neuromechanical Modeling of Zebrafish Locomotion Etienne Hugues1, Donald P Knudsen2, John A Arsenault2, Donald M O'Malley2, Jorge V Jose1 1SUNY at Buffalo 2Northeastern University 110. Population coding of reaction time performance in rat motor cortex and dorsal striatum Mark Laubach, Nandakumar S Narayanan, Eyal Y Kimchi Yale University

Networks/circuits 111. Odor identity and concentration coding in the model of the locust olfactory system Collins G Assisi1, Mark Stopfer2, Gilles Laurent3, Maxim Bazhenov1 1The Salk Institute for Biological Studies 2NIH-NICHD 3California Institute of Technology 112. Biologically realistic neural inhibition in arbitrary neural circuits Christopher Parisien1, Charles H. Anderson2, Chris Eliasmith1 1University of Waterloo 2Washington University in St. Louis 113. On the Dynamics of Electrically-coupled Neurons with Inhibitory Synapses Juan Gao1, Philp Holmes2 1Department of Mechanical and Aerospace Engineering 2Department of Mechanical and Aerospace Engineering & Program in Applied and Computational Mathematics. Princeton University 114. Computational consequences of lamina-specific structure in cortical microcircuit models Stefan Haeusler, Wolfgang Maass Institute for Theoretical Computer Science, Graz University of Technology, Austria 115. Pulse Packet Interaction in Associative Synfire Chain Kazuya Ishibashi1, Kosuke Hamaguchi2, Masato Okada3 1Univ. of Tokyo / JST PRESTO 2RIKEN BSI 3Univ. of Tokyo / JST PRESTO / RIKEN BSI 116. A theory of object recognition:A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex Thomas Serre, Minjoon Kouh, Charles Cadieu, Ulf Knoblich, Gabriel Kreiman, Tomaso Poggio MIT 117. Analysis of Cortical Microcircuits on the Systems Level Robert Legenstein, Wolfgang Maass Technische Universitaet Graz 118. One-shot learning of behavioral sequences through hippocampal phase precession: A functional hypothesis on short-term synaptic plasticity Christian Leibold1, Kay Thurley1, Anja Gundlfinger2, Robert

Schmidt1, Dietmar Schmitz2, Richard Kempter1 1Institute for Theoretical Biology HU Berlin 2NSRC Charite Berlin 119. Mean Field Theory with Cross-Correlations for a Cortical Network Model Alexander Lerchner1, John Hertz2 1Laboratory of Neuropsychology, NIMH/NIH/DHHS 2NORDITA 120. Robust Propagation of Bursts in Noisy Heterogeneous Synfire Chains Meng-Ru Li, Henry Greenside Duke University

Sensory/perception/attention 121. Do within modality and cross-modality sensory integration follow the same rules? Ulrik R Beierholm1, Steven R Quartz1, Ladan Shams2 1Caltech 2UCLA - Dept. of Psych. 122. Spike timing in mechanoreceptive afferent fibers can be predicted using integrate-and-fire mechanisms. Sliman J Bensmaia1, Arun P Sripati2 1Johns Hopkins University 2Center for the Neural Basis of Cognition 123. Brain-Inspired Neural Model of Visual Attention for Multiple Object Tracking Roman Borisyuk1, Yakov Kazanovich2 1University of Plymouth, United Kingdom 2Institute Mathematical Problems in Biology, Russian Academy of Sciences 124. STATISTICS OF SYLLABLE PATTERNS IN PRODUCED SONGS PREDICT AUDITORY RESPONSES IN HVC OF BENGALESE FINCHES Kristofer E Bouchard, Michael S Brainad UCSF 125. The Role of Memory in Guiding Attention Ran Carmi, Laurent Itti University of Southern California 126. Optimal Spatial Pooling of Neural Population Responses in the Visual Cortex Yuzhi Chen, Zhiyong Yang, Wilson S Geisler, Eyal Seidemann Institute for Neuroscience and Center for Perceptual Systems, University of Texas at Austin, 78712 127. Characterizing contrast adaptation in a population of cat primary visual cortical neurons using Fisher information Colin WG Clifford1, Szonya Durant1, Nathan A Crowder2, Nicholas SC Price2, Michael R Ibbotson2 1University of Sydney 2Australian National University 128. Multivariate Analysis of Frontal Eye Field Activity during Visual Search Jeremiah Y Cohen1, Pierre Pouget2, Chenchal Rao2, Jeffrey D

Schall2, Andrew F Rossi2 1Vanderbilt Brain Institute 2Vanderbilt University Department of Psychology 129. Spectral receptive field properties explain shape selectivity in V4 Stephen V David1, Benjamin Y Hayden2, Jack L Gallant2 1University of Maryland 2University of California, Berkeley 130. Robustness to reverberation of directionally-sensitive neurons in the inferior colliculus Sasha Devore, Bertrand Delgutte Harvard-MIT Division of Health Science and Technology 131. Extracellular Electrode Detection Range and Sampling Bias for Cat Visual Cortex Carl Gold1, Cyrille Girardin2, Rodney Douglas2, Christof Koch1 1Computation and Neural Systems, California Institute of Technology 2Institute of Neuroinformatics, Swiss Federal

Institute of Technology (ETH) 132. Hierarchical subunit model for disparity-selective complex cells in V1 Ralf M Haefner, Bruce G Cumming LSR/NEI/NIH 133. Sound discrimination in awake head-fixed rats Tomas Hromadka, Anthony M Zador Watson School of Biological Sciences, Cold Spring Harbor Laboratory 134. A model for stimulus competition and selective visual attention in area V4 Etienne Hugues1, Scott A Hill2, Paul H Tiesinga3, Jorge V José1 1SUNY at Buffalo 2CIRCS, Northeastern University 3University of North Carolina at Chapel Hill 135. Functional topology of attention in the pulvinar Oliver Hulme1, Justin Chumbley1, Simon B Eickhoff2, Simon

Prince1, Stuart Shipp1 1University College London 2Research Center Julich 136. Feedback-mediated facilitation from the "far" receptive field surround of macaque V1 neurons Jennifer M Ichida1, Lars Schwabe2, Paul C Bressloff3, Alessandra Angelucci1 1Moran Eye Center, University of Utah, Salt Lake City, UT, USA 2Electrical Enginering & Computer Science, TU Berlin, Germany 3Department of Mathematics, University of Utah, Salt Lake City, UT, USA 137. Optimal strategies for active perception Santiago Jaramillo, Barak A Pearlmutter National University of Ireland, Maynooth 138. Information-based fMRI analysis for predefined regions of interest Nikolaus Kriegeskorte, Peter Bandettini NIMH 139. Two new visual areas in human lateral occipital cortex Jonas Larsson, David J Heeger Dept. of Psychology & Center for Neural Science, NYU 140. An information theoretic approach to detecting and discriminating mouse communication sounds Robert C Liu1, Christoph E Schreiner2 1Emory University 2UCSF 141. Effect of MST microstimulation on MT motion responses. Christin H McCool, Ken Britten University of California, Davis 142. Sparse reverse correlation sequences result in improved tuning functions in V4. Jude F Mitchell, John H Reynolds The Salk Institute

Poster presentations, Session II

Cellular/synaptic 143. Non-Uniform Passive Membrane Property in Dendrite Estimated by Fitting Multi-Compartment Model to Voltage Imaging Data Toshiaki Omori1, Toru Aonishi2, Hiroyoshi Miyakawa3, Masashi

Inoue3, Masato Okada4 1PRESTO, Japan Science and Technology Agency / RIKEN 2Tokyo Institute of Technology / RIKEN 3Tokyo University of Pharmacy and Life Science 4The University of Tokyo / PRESTO, Japan Science and Technology Agency / RIKEN 144. Variations in response sensitivity and intrinsic properties of cortical neurons Michael J Pesavento, David J Pinto Departments of Biomedical Engineering and Neurobiology & Anatomy, University of Rochester School of Medicine, Rochester, NY 145. Nonlinear interaction between shunting and adaptation controls a switch between integration and coincidence detection in pyramidal neurons Steven A Prescott1, Stéphanie Ratté2, Yves De Koninck3, Terrence J Sejnowski4 1Computational Neurobiology Laboratory, The Salk Institute, La Jolla, CA 92037 2Département de physiologie, Université de Montréal, Montréal, Québec, Canada H3C 3J7 3Division de Neurobiologie Cellulaire, Centre de Recherche Université Laval Robert-Giffard, Québec, Québec, Canada G1J 2G3, and Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada H3A 1Y6 4Computational Neurobiology Laboratory, The Salk Institute, La Jolla, CA 92037, and Division of Biological Sciences, University of San Diego, La Jolla, CA 92093 146. SPATIAL INTEGRATION OF AMPA- AND GABA-TYPE EXCITATION IN HYPOTHALAMIC GNRH NEURONS Carson B Roberts, Kelly J Suter Emory University 147. Dendritic Darwinism: Artificial evolution of neurons optimized for specific computations Klaus M. Stiefel, Terrence J. Sejnowski CNL, The Salk Institute 148. Creation and reduction of a morphologically detailed model of a leech heart interneuron Anne-Elise Tobin1, Ronald L Calabrese2 1Brandeis University 2Emory University

Coding/computation 149. Entorhinal Input and the Remapping of Hippocampal Place Fields Joseph D Monaco1, Isabel A Muzzio2, Liat Levita2, L F Abbott1 1Center for Theoretical Neuroscience, Columbia University 2Center for Neurobiology & Behavior, Columbia University 150. Spatial and temporal organization of glomerular

representation in the moth antennal lobe Shigehiro Namiki1, Ryohei Kanzaki2 1the University of Tsukuba 2the University of Tokyo 151. Efficient Neural Burst Analysis Rama Natarajan1, Farzan Nadim2 1University of Toronto 2Rutgers University and New Jersey Institute of Technology 152. Dynamical contrast gain control mechanisms in a layered model of the primary visual cortex Laurent U Perrinet, Jens Kremkow, Alexandre Reynaud, Frédéric Y Chavane INCM/CNRS 153. An information-theoretic generalization of spike-triggered average and covariance analysis Jonathan W Pillow1, Eero P Simoncelli2 1Gatsby Computational Neuroscience Unit, UCL 2HHMI and New York University 154. Visual acuity in the presence of fixational eye movements Xaq Pitkow1, Haim Sompolinsky2, Markus Meister1 1Harvard University 2Hebrew University 155. Natural scene statistics predict that larger ganglion cells should have relatively smaller surrounds Charles P Ratliff, Peter Sterling, Vijay Balasubramanian University of Pennsylvania 156. Solving the stereo correspondence problem with hybrid position and phase disparity detectors Jenny Read1, Bruce Cumming2 1Newcastle University 2National Eye Institute 157. Extracting low-dimensional task representations from neural signals James Rebesco1, Sara A Solla2, Lee E Miller1 1Department of Physiology, Northwestern University 2Department of Physics and Astronomy, Northwestern University 158. Contrast gain control in the LGN optimizes information transfer, but may not require any dynamic adaptation process Kate S Denning, Pamela Reinagel UCSD 159. How behavioral constraints may determine optimal sensory representations Emilio Salinas Wake Forest University School of Medicine 160. The scaling of ‘Winner Takes All’ accuracy with the population size Maoz Shamir Center for Bio-dynamics, Boston University 161. Temporal Invariance and Predictive Coding Jonathan Shaw University of Rochester

162. Probing the structure of multi-neuron firing patterns in the primate retina using maximum entropy methods Jonathon Shlens1, Greg D Field1, Jeff L Gauthier1, Matthew I

Grivich2, Dumitru Petrusca2, Alexander Sher2, Alan M Litke2, EJ Chichilnisky1 1Salk Institute 2UC Santa Cruz 163. Hybrid Discrete/Continuous Models of Brain Dynamics: Estimation from Spikes Lakshminarayan Srinivasan1, Uri T Eden2, Sanjoy K Mitter3, Emery N Brown4 1MIT Department of Electrical Engineering & Computer Science/ Harvard Medical School, Health Sciences & Technology Track/ Massachusetts General Hospital 2HMS-MIT HST/MGH 3MIT EECS Laboratory of Information & Decision Systems 4MIT Deptartment of Brain & Cognitive Sciences / HMS-MIT HST / MGH Anesthesia & Critical Care 164. Quantifying The Linear And Nonlinear Components Of A Neuronal Response Using Matching Pursuit Regression With A Redundant Dictionary Of Kernels Pramodsingh H Thakur, Paul J Fitzgerald, Sung S Kim, Steven S Hsiao Johns Hopkins University 165. Distinct roles of synaptic connectivity and refractoriness on the spike-based and rate-based population decoding Taro Toyoizumi1, Kazuyuki Aihara2, Shun-ichi Amari3 1Department of Complexity Science and Engineering, University of Tokyo 2Institute of Industrial Science, University of Tokyo 3RIKEN Brain Science Institute 166. Sniffing cycle-based odor coding in the anterior olfactory cortex Naoshige Uchida, Zachary F Mainen Cold Spring Harbor Laboratory 167. Differences in processing of low- and high-order image statistics revealed by classification images extracted via regularized regression Jonathan D Victor, Ana A Ashurova, Mary M Conte Weill Medical College of Cornell University 168. An Adaptive Method of Spatiotemporal Receptive Field Estimation Michael T Wahl UC Berkeley Department of Physics 169. Maximum likelihood decoding of moving stimuli using divisive normalization line attractor neural networks Robert C Wilson, Leif H Finkel University of Pennsylvania 170. Behaviorally-dependent information processing in a songbird circuit required for vocal plasticity Brian D Wright1, Mimi H Kao2, Allison J Doupe3 1UCSF, Sloan-Swartz Center for Theoretical Neurobiology 2UCSF, Dept. of Physiology 3UCSF, Depts. of Physiology and Psychiatry

Decisions/cognition 171. A Simulation of the Uniform Selection Hypothesis in a Delayed-response Task

Ahmed A. Moustafa1, Anthony S. Maida2 1Institute of Cognitive Science, University of Louisiana at Lafayette, Lafayette, LA 70504 2Center for Advanced Computer Studies and Institute of Cognitive Science, University of Louisiana at Lafayette, Lafayette, LA 70504 172. Tetrode recordings in dorsal and median raphe nuclei in awake behaving rats Sachin P Ranade, Zachary F Mainen CSHL 173. A Credit Assignment Algorithm for Composite Visuo-Motor Behaviors Constantin A Rothkopf, Dana H Ballard University of Rochester 174. State-dependent matching law in stochastic gradient ascent Yutaka Sakai1, Tomoki Fukai2 1Human Informatics Course, Faculty of Engineering, Tamagawa University, Japan 2Brain Science Institute, RIKEN, Japan 175. Different subregions of human striatum encode appetitive and aversive outcomes in mixed prospect predictive learning of money. Ben Seymour1, Nathaniel Daw2, Peter Dayan2, Tania Singer3, Ray Dolan1 1Wellcome Department of Imaging Neuroscience, UCL 2Gatsby Compuational Neuroscience Unit, UCL 3Institute of Cognitive Neuroscience, UCL 176. From Chaos to Self-organization, and from Firing Fields to Place Fields. A New Hypothesis on Hippocampal Neurodynamics Renan Vitral NIPAN. Department of Physiology. Biological Sciences Institute. Federal University of Juiz de Fora, BR. 177. Time integration in a perceptual decision task: adding and subtracting brief pulses of evidence in a recurrent cortical network model Kong-Fatt Wong1, Alexander C Huk2, Michael N Shadlen3, Xiao-Jing Wang1 1Brandeis University 2University of Texas at Austin 3University of Washington, Seattle 178. Bidirectional spike-timing dependent plasticity of inhibitory transmission in the hippocampus Jake Ormond, Melanie A Woodin University of Toronto 179. Neural activities of resolution of state uncertainty in a partially-observable maze task Wako Yoshida, Shin Ishii Nara Institute of Science and Technology 180. Dissociation of accuracy and reaction time in a two alternative odor mixture discrimination task Hatim A Zariwala, Naoshige Uchida, Adam Kepecs, Zachary F Mainen Cold Spring Harbor Laboratory, Cold Spring Harbor, NY

Learning/plasticity

181. Conserving mean activity through adaptive inhibition leads to temporal sharpening when combined with Hebbian enhancement of excitatory connections Samat B Moldakarimov1, James L McClelland2, Bard G Ermentrout1 1University of Pittsburgh 2Carnegie Mellon Unibersity 182. Discrimination Training and Neural Coding of Speech Sounds in Rat Primary Auditory Cortex Crystal T Novitski, YeTing H Chen, Amanda C Puckett, Vikram

Jakkamsetti, Claudia A Perez, Matthew S Perry, Ryan S

Carraway, Michael P Kilgard The University of Texas at Dallas 183. Laminar model for cortical development with an emphasis on the macaque visual system Andrew M Oster, Paul C Bressloff University of Utah 184. Arbitrary Functions Learning with Neural Network Model Based on Spike-Timing Dependent Plasticity Yefei Peng, Paul W. Munro University of Pittsburgh 185. Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects Jean-Pascal Pfister, Wulfram Gerstner Brain-Mind and I&C, EPFL 186. Online Learning in a Model Neural Integrator Srinivas C Turaga1, Haim Sompolinsky2, H. Sebastian Seung1 1MIT 2The Hebrew University 187. A Hebbian reinforcement learning algorithm reproducing monkey performances in visuo-motor learning task. Eleni Vasilaki1, Stefano Fusi2, Xiao-Jing Wang3, Walter Senn1 1Institute of Physiology, University of Bern, Switzerland 2Institute for Neuroinformatics (INI), ETH, Zurich, Switzerland 3Volen Center for Complex Systems, Brandeis University, MA 188. Eyelid conditioning, timing and the cerebellum Horatiu Voicu, Tatsuya Ohyama, Michael D Mauk UTH Health Science Center 189. Experience-Induced Arc Sharpens Orientation Ensembles in Visual Cortex Kuan Hong Wang, Ania Majewska, Mriganka Sur, Susumu Tonegawa M.I.T. 190. Memory recall based on rebound conductances Daniel Z Wetmore, Eran A Mukamel, Mark J Schnitzer Stanford University 191. Dendritic Morphology and Storage Capacity in Hippocampal Pyramidal Neurons Xundong Wu, Bartlett W Mel University of Southern California 192. Retinal lesion-induced receptive field reorganization in primary visual cortex is spike timing dependent

Joshua M Young1, Bogdan Dreher2, Klaus Obermayer1 1Neural Information Processing Group, Department of Computer Science, Berlin University of Technology, Germany / Bernstein Center for Computational Neuroscience Berlin, Germany 2Institute for Biomedical Research, The University of Sydney, Australia 193. Learning by message-passing in networks of discrete synapses Riccardo Zecchina1, Alfredo Braunstein2 1International Centre for Theoretical Physics (ICTP) 2Institute for Scientific Interchange (ISI) 194. Supervised STDP as a Means to Initiate and/or Coordinate Neuronal Maps Leo van Hemmen Physik Department, Technical University of Munich

Motor/sensorimotor 195. High frequency stimulation of the subthalamic nucleus restores thalamic relay reliability in a computational model Jonathan E Rubin1, Yixin Guo2, Cameron McIntyre3, Kresimir

Josic4, David Terman5 1University of Pittsburgh 2Drexel University 3Cleveland Clinic 4University of Houston 5The Ohio State University 196. A biophysical approach to behavioral neuroscience in C. elegans Aravinthan Samuel Harvard University 197. High-Fidelity Coding of Single Trials by Neurons in the Macaque Frontal Pursuit Area David Schoppik, Katherine I Nagel, Stephen G Lisberger HHMI/UCSF 198. Dimensionality and Dynamics in the Motor Behavior of C. elegans Greg J Stephens, William Bialek, William S Ryu Princeton University 199. A local tuning model predicts adaptation rate and degree of generalization of visuomotor rotation learning Hirokazu Tanaka1, Terrence J Sejnowski1, John W Krakauer2 1Computational Neurobiology Laboratory, Salk Institute 2Motor Performance Laboratory, Columbia University 200. Space-time separability in goal-oriented motion generation Elizabeth B Torres, Richard Andersen CALTECH 201. Non-Parametric Methods for the Modeling of Neural Point Processes Wilson Truccolo, John P Donoghue Brown University, Neuroscience Department 202. A fundamental ambiguity in model-based adaptive control Douglas B Tweed, Mohamed N Abdelghani University of Toronto

Networks/circuits 203. Circuit geometry and the representation of time in cerebellar networks Eran A Mukamel, Mark J Schnitzer Stanford University 204. SENSORY ENCODING, RELATIVE SPIKE-TIMING, AND NOISE: LESSONS FROM THE HERMISSENDA EYE William H Nesse1, Christopher R Butson2, Gregory A Clark2 1University of Utah Department of Mathematics 2University of Utah Department of Bioengineering 205. Smooth and Lurching Pulses in Two-Layer Thalamocortical-Reticular Integrate-and-Fire-or-Burst Networks William Nesse, Paul C Bressloff University of Utah 206. Inferring causal subnetworks using point process models Duane Q Nykamp University of Minnesota 207. An Algebraic Approach to the Analysis of Network Functional Connectivity: Application on data from the basal ganglia Naama Parush, Gali Heimer, Hagai Bergman, Naftali Tishby Hebrew University 208. Brute-force computational exploration of calcium-based activity sensors in a model pattern-generating network Astrid A Prinz Emory University 209. Retrieving the identity of a visual object while keeping information about its position in a model of IT cortex Yasser Roudi, Alessandro Treves Cognitive Neuroscience sector, SISSA 210. Distal gap junctions and active dendrites maximize stable, phase-locked network dynamics Fernanda Saraga1, Leo Ng2, Frances K Skinner3 1Department of Zoology, Department of Physiology, Toronto Western Research Institute, University Health Network, University of Toronto 2Engineering Science Program, University of Toronto 3Toronto Western Research Institute, University Health Network, Department of Medicine (Neurology), Department of Physiology, Institute of Biomaterials and Biomedical Engineering, University of Toronto 211. Periodic Bursting in Two Identical Coupled Cell Systems LieJune Shiau1, Marty Golubitsky2, Kresimir Josic2 1University of Houston-Clear Lake 2University of Houston 212. Why Should Cortical Connectivity be Sparse and Predominantly Excitatory? Armen Stepanyants, Darin R La Sota Northeastern University 213. Topological design of cortical networks that display power-law statistics of neuronal avalanches Jun-nosuke Teramae, Tomoki Fukai

RIKEN Brain Science Institute 214. Initial neuronal group activity is precisely maintained during propagation within neuronal avalanches in vitro Tara C Thiagarajan, Dietmar Plenz NIMH/NIH 215. Comparison of Neural Circuits that Estimate Temporal Derivatives Bryan P Tripp, Chris Eliasmith University of Waterloo 216. The Role of Precise Thalamic Spike Timing in Generating Nonlinear Cortical Responses in the Rat Vibrissa System Roxanna M Webber1, Garrett B Stanley2 1Harvard - MIT Division of Health Sciences and Technology 2Division of Engineering and Applied Sciences, Harvard University 217. Synaptic Mechanisms of Thalamocortical Auditory Processing Mike Wehr University of Oregon 218. Recurrent Network Models for Working Memory of Temporal Sequences Olivia L White1, Avigail Ben Or2, Haim Sompolinsky2 1MIT, Department of Physics 2Hebrew University 219. Stability analysis of cooporative algorithms Junmei Zhu Computer Science Department, University of Memphis

Sensory/perception/attention 220. Temporal Processing and Adaptation in the Zebra Finch Auditory Forebrain Katherine Nagel, Tatyana Sharpee, Allison J Doupe UCSF 221. Choice probabilities in V2 reflect task strategy, as measured psychophysically Hendrikje Nienborg, Bruce G Cumming Laboratory of Sensorimotor Research/NEI/NIH 222. Visual working memory and attention in early visual cortex Shani Offen, Denis Schluppeck, David J Heeger NYU 223. Early Visual Responses: More than "Low-Level Features" Cheryl A Olman University of Minnesota 224. Bayesian model learning in human visual perception Gergo Orban1, Jozsef Fiser2, Richard N Aslin3, Mate Lengyel4 1Collegium Budapest 2Volen Center for Complex Systems, Brandeis University 3Department of Brain and Cognitive Sciences, University of Rochester 4Gatsby Computational Neuroscience Unit, University College London

225. State-dependence differences in evoked responses in auditory cortex Gonzalo H Otazu, Anthony M Zador Cold Spring Harbor Laboratory 226. Illusory Percepts from Auditory Adaptation Lucas C. Parra1, Barak A. Pearlmutter2 1City College New York 2Hamilton Institute, NUI Maynooth, Ireland 227. Physics of active touch: What does the vibrissa system sense? Jason Ritt, Christopher I Moore McGovern Institute for Brain Research, MIT 228. Using Illusions to Model Top-Down Biasing in V1 Robert Rohrkemper, Hava Siegelmann UMASS 229. Inferring Neural Circuitry from Modulation Metrics: Lessons from a Computational Model of Primary Visual Cortex Jim Wielarrd, Paul Sajda Columbia University 230. Adaptation in stimulus amplitude coding in rat barrel cortex Jan WH Schnupp, Simon SM Ho, Jose A Garcia-Lazaro Oxford University 231. Dissociation of Stimulus-Driven and Attention-Driven Activity in Macaque Primary Visual Cortex Jitendra Sharma1, Beau Cronin1, Klaus Wimmer2, Konrad

Körding1, James Schummers1, Klaus Obermayer2, Mriganka Sur1 1Dept. of Brain and Cognitive Sciences, Picower Center for Learning and Memory, MIT, Cambridge, MA, USA 2Dept. of Computer Science and Electrical Engineering, Bernstein Center for Computational Neuroscience, Berlin, Univ. of Technology, Berlin, Germany 232. Essential Features of Temporal Processing in the Songbird Auditory Forebrain Tatyana O Sharpee, Katherine Nagel, Allison J Doupe University of California, San Francisco 233. A theoretical model of cochlear processing improves simulated cochlear implant hearing Evan C Smith, Lori L Holt Carnegie Mellon University 234. Interactions between eye movements, receptive fields, and processing streams in primary visual cortex of alert monkeys Max Snodderly1, Igor Kagan2, Moshe Gur3 1Medical College of Georgia 2Caltech 3Technion, Israel 235. Heterogenous firing rate dependencies in simultaneously recorded neural populations in cat area 17 Martin A Spacek1, Timothy J Blanche2, Nicholas V Swindale1 1University of British Columbia 2Hanse-Wissenschaftskolleg, Brain Research Institute, University of Bremen

236. Adaptation within a Bayesian Framework for Perception Alan A Stocker, Eero P Simoncelli Howard Hughes Medical Institute and Center for Neural Science, New York University 237. Single unit and local field potential characterization of contrast dependent responses in area V4 of the macaque Kristy A Sundberg, Jude F Mitchell, John H Reynolds The Salk Institute 238. Wireless Multi-Unit Recording from Unconstrained Animals Tobi Szuts1, Edward Soucy2, Alan Litke3, Athanassios Siapas4, Markus Meister2 1Program in Biophysics, Harvard University 2Department of Molecular and Cellular Biology, Harvard University 3Santa Cruz Institute for Particle Physics, University of California Santa Cruz 4Division of Biology, California Institute of Technology 239. Modulation of auditory responses by modality-specific attention in rat primary auditory cortex Lung-Hao Tai, Anthony M Zador Cold Spring Harbor Laboratory 240. Multiple S-cone pathways in the macaque visual system Chris Tailby1, Samuel G Solomon2, Peter Lennie1 1New York University 2University of Sydney 241. ATTENTIONAL MODULATION OF STIMULUS COMPETITION IN A LARGE-SCALE MODEL OF THE VISUAL PATHWAY Calin I Buia, Paul H Tiesinga University of North Carolina, Chapel Hill, Physics & Astronomy 242. Rapid Adaptation from the Non-Classical Receptive Field in area MT of the Macaque Pascal Wallisch, Gopathy Purushothaman, David C Bradley University of Chicago 243. Observers' decisions in a simple visual task are consistent with Bayesian processing of early perceptual uncertainty Louise Whiteley, Maneesh Sahani Gatsby Computational Neuroscience Unit, UCL 244. A Bayesian View of Sensory Conflicts in Decision-Making Angela J Yu1, Peter Dayan2, Jonathan D Cohen1 1Princeton University 2Gatsby Computational Neuroscience Unit, UCL 245. Neuronal sensitivity and choice probability in macaque VIP during a heading discrimination task Tao Zhang, Ken H Britten Center for Neuroscience and Section of NPB, UC Davis

Author Index (main meeting only)

L F Abbott lfa2103@columbia.edu 149 Mohamed N Abdelghani mohamed.abdelghani@utoronto.ca 102, 202 Ad Aertsen aertsen@biologie.uni-freiburg.de 72 Nick Aggelopoulos na@psy.ox.ac.uk 57 Blaise Aguera y Arcas blaise@sandcodex.com 63 Kazuyuki Aihara aihara@sat.t.u-tokyo.ac.jp 165 Emre Aksay eaksay@Princeton.EDU 107 Mark V. Albert mva6@cornell.edu 43 Jose-Manuel Alonso jalonso@mail.sunyopt.edu 48 Shun-ichi Amari amari@brain.riken.go.jp 78, 165 Maxime Ambard mambard@yahoo.fr 44 Ramin Amirnovin ramirnovn@partners.org 55 Richard Andersen andersen@vis.caltech.edu 30, 200 Charles H. Anderson cha@wustl.edu 45, 56, 112 Ralph Andrzejak ralphandrzejak@yahoo.de 80 Dora Angelaki 19 Alessandra Angelucci alessandra.angelucci@hsc.utah.edu 136 Toru Aonishi aonishi@dis.titech.ac.jp 143 Majid Arabgol arabgol@ipm.ir 90 John A Arsenault arsenault.j@neu.edu 109 Hiroki Asari asari@cshl.edu 46 Ana A Ashurova ma20008399@aol.com 167 Richard N Aslin aslin@bcs.rochester.edu 224 Collins G Assisi collins@salk.edu 111 David Attwell 3 Bruno B Averbeck baverbeck@cvs.rochester.edu 28 Baktash Babadi baktash@ipm.ir 90 Wyeth Bair wyeth@physiol.ox.ac.uk 62 Vijay Balasubramanian vijay@physics.upenn.edu 58, 69, 155 Dana H Ballard dana@cs.rochester.edu 66,173 Peter Bandettini bandettini@nih.gov 138 Maxim Bazhenov bazhenov@salk.edu 111 Jeffrey M Beck jbeck@bcs.rochester.edu 47,75 Suzanna Becker becker@mcmaster.ca 93 Bardia F Behabadi behabadi@usc.edu 39 Ulrik R Beierholm beierh@caltech.edu 121 Marina A Belova mab2058@columbia.edu 91 Avigail Ben Or avigail@pob.huji.ac.il 218 Sliman J Bensmaia sliman@jhu.edu 122 Hagai Bergman hagaib@md.huji.ac.il 207 Michael J Berry berry@Princeton.EDU 69 Guo-Qiang Bi gqbi@pitt.edu 92 William Bialek wbialek@princeton.edu 31,198 Hugh T Blair blair@psych.ucla.edu 6 Timothy J Blanche cosyne@timblanche.mm.st 235 Roman Borisyuk rborisyuk@plymouth.ac.uk 123 Kristofer E Bouchard kris@phy.ucsf.edu 124 David C Bradley bradley@uchicago.edu 242 Michael S Brainad msb@phy.ucsf.edu 124 David H. Brainard brainard@psych.upenn.edu 58 Alfredo Braunstein braunstein@isi.it 193 Paul C Bressloff bressloff@math.utah.edu 136, 183, 205 Ken H Britten khbritten@ucdavis.edu 141, 245 Carlos D Brody brody@cshl.edu 88 Emery N Brown brown@neurostat.mgh.harvard.edu 55, 163 Nicolas Brunel nicolas.brunel@univ-paris5.fr 95 Calin I Buia buia@physics.unc.edu 241 Neil Burgess 5 Kevin Burrage kb@maths.uq.edu.au 41 Christopher R Butson cbutson@utah.edu 204 Daniel A Butts dbutts@deas.harvard.edu 48 Patrick A Byrne byrne@psychology.mcmaster.ca 93 Charles Cadieu cadieu@berkeley.edu 116 Xinying Cai xinying.cai@asu.edu 94 Ronald L Calabrese rcalabre@biology.emory.edu 148 Ran Carmi carmi@usc.edu 125 Ryan S Carraway rsc041000@utdallas.edu 182 Frances S Chance fchance@uci.edu 68 Steven M Chase schase@bme.jhu.edu 49 Frédéric Y Chavane Frederic.Chavane@incm.cnrs-mrs.fr 152 Ricardo Chavarriaga ricardo.chavarriaga@a3.epfl.ch 50 YeTing H Chen ytc013000@utdallas.edu 182 Yuzhi Chen chen@mail.cps.utexas.edu 126

EJ Chichilnisky ej@salk.edu 25, 162 Justin Chumbley jrchumbley@yahoo.com 135 Gregory A Clark Greg.Clark@utah.edu 204 Colin WG Clifford colinc@psych.usyd.edu.au 127 Jeremiah Y Cohen jeremiah.y.cohen@vanderbilt.edu 128 Jonathan D Cohen jdc@princeton.edu 244 Mary M Conte mmconte@med.cornell.edu 167 Beau Cronin bcronin@MIT.EDU 231 Beau D Cronin bcronin@mit.edu 51 Patrick Crotty prc9m@virginia.edu 4 Nathan A Crowder nathan.crowder@anu.edu.au 127 Bruce G Cumming bgc@lsr.nei.nih.gov 132, 156, 221 Emanuele Curti ec2334@columbia.edu 79 Mohammad Dastjerdi dastjerdi@ccs.fau.edu 52 Stephen David svd@umd.edu 77 Stephen V David svd@umd.edu 129 Nathaniel Daw daw@gatsby.ucl.ac.uk 175 Peter Dayan dayan@gatsby.ucl.ac.uk 7, 21, 36, 41, 82, 175, 244 Yves De Koninck yves.dekoninck@crulrg.ulaval.ca 145 Gregory C DeAngelis gregd@cabernet.wustl.edu 45 Adenike O Deane-Pratt adenike.turner@ucl.ac.uk 76 Gustavo Deco gustavo.deco@upf.edu 80 Chris Deister chris.deister@utsa.edu 40 Bertrand Delgutte Bertrand_Delgutte@meei.harvard.edu 130 Kate S Denning kdenning@biomail.ucsd.edu 158 Gaelle Desbordes gdesbord@cns.bu.edu 15 Sasha Devore sashad@mit.edu 130 Eizaburo Doi edoi@cnbc.cmu.edu 53 Ray Dolan r.dolan@fil.ion.ucl.ac.uk 175 Dawei W Dong dawei@dove.ccs.fau.edu 52, 54 John P Donoghue John_Donoghue@Brown.edu 201 Rodney Douglas rjd@ini.phys.ethz.ch 131 Allison J Doupe ajd@phy.ucsf.edu 24, 170, 220, 232 Bogdan Dreher bogdand@anatomy.usyd.edu.au 192 Szonya Durant szonyad@psych.usyd.edu.au 127 Alexander Ecker alexander.ecker@tuebingen.mpg.de 26 Uri T Eden tzvi@neurostat.mgh.harvard.edu 55, 163 Simon B Eickhoff S.Eickhoff@fz-juelich.de 135 Chris Eliasmith celiasmith@uwaterloo.ca 112, 215 Bard G Ermentrout bard@math.pitt.edu 181 Emad N Eskandar eeskandar@partners.org 55 Adrienne L Fairhall fairhall@u.washington.edu 63 Michael Fee 22 Claudia E Feierstein feierste@cshl.edu 81 David J. Field djf3@cornell.edu 43 Greg D Field gfield@salk.edu 162 Michael S. Fine msf1@cec.wustl.edu 103 Leif H Finkel leif@neuroengineering.upenn.edu 169 Brian J. Fischer brian@etho.caltech.edu 56 Jozsef Fiser fiser@brandeis.edu 224 Paul J Fitzgerald pfitz@mbi.mb.jhu.edu 164 Kristen P Fortney virian@yahoo.com 104 Leonardo Franco lfranco@lcc.uma.es 57 Eric S Frechette frechette@ucsd.edu 25 Michael Freed michael@retina.anatomy.upenn.edu 69 Itzhak Fried ifried@mednet.ucla.edu 70 Robert C Froemke rfroemke@phy.ucsf.edu 12 Naotaka Fujii na@fujiis.com 67 Tomoki Fukai tfukai@brain.riken.jp 174,213 Stefano Fusi fusi@ini.unizh.ch 79,187 Christopher V Gabel gabel@fas.harvard.edu 105 Jack L Gallant gallant@socrates.berkeley.edu 129 Surya Ganguli surya@faure.ucsf.edu 11 Juan Gao jgao@princeton.edu 113 Jose A Garcia-Lazaro jagl@physiol.ox.ac.uk 230 Daniel Gardner dgardner@med.cornell.edu 59 Patrick Garrigan pg@sas.upenn.edu 58 Jeff L Gauthier gauthier@salk.edu 162 Wilson S Geisler geisler@psy.utexas.edu 126 Wulfram Gerstner wulfram.gerstner@epfl.ch 50, 185 Kaushik Ghose kaushik.ghose@gmail.com 106 Cyrille Girardin cyrilleg@ini.phys.ethz.ch 131 Paul W. Glimcher glimcher@cns.nyu.edu 37 Carl Gold carlg@caltech.edu 131 David H Goldberg dhg2002@med.cornell.edu 59 Joshua A Goldberg joshua.goldberg@utsa.edu 40 Mark S Goldman markg@princeton.edu 107 Tim Gollisch gollisch@fas.harvard.edu 60

Marty Golubitsky mg@math.uh.edu 211 Geoffrey J Goodhill g.goodhill@uq.edu.au 41 Arnulf B.A. Graf arnulf.graf@nyu.edu 61 Michael Graupner michael.graupner@univ-paris5.fr 95 Ann M Graybiel graybiel@MIT.EDU 67 Henry Greenside hsg@phy.duke.edu 120 Matthew I Grivich mgrivich@scipp.ucsc.edu 25, 162 Robert Guetig guetig@cc.huji.ac.il 96 Anja Gundlfinger anja.gundlfinger@charite.de 118 Yixin Guo yigst@math.ohio-state.edu 195 Moshe Gur mogi@bm.technion.ac.il 234 Ralf M Haefner haefnerr@nei.nih.gov 132 Stefan Haeusler haeusler@igi.tugraz.at 114 Kosuke Hamaguchi hammer@brain.riken.jp 115 Michael E Hasselmo hasselmo@bu.edu 99 Michael Hausser m.hausser@ucl.ac.uk 73 Benjamin Y Hayden hayden@neuro.duke.edu 129 Jiping He hjp@asu.edu 94 David J Heeger david.heeger@nyu.edu 139, 222 Nicolas Heess nmo@physiol.ox.ac.uk 62 Gali Heimer galih@md.huji.ac.il 207 John Hertz hertz@nordita.dk 119 Andreas VM Herz a.herz@biologie.hu-berlin.de 32 Scott A Hill shill@tower.par64.org 134 Shizuko Hiryu etd1101@mail4.doshisha.ac.jp 35 Simon SM Ho simon.ho@oriel.ox.ac.uk 230 Philip J Holmes pholmes@Math.Princeton.EDU 86, 113 Lori L Holt lholt@andrew.cmu.edu 233 Sungho Hong shhong@u.washington.edu 63 Timothy K Horiuchi timmer@isr.umd.edu 106 Toshihiko Hosoya hosoya@brain.riken.jp 64 Tomas Hromadka hromadka@cshl.edu 133 Steven S Hsiao steven.hsiao@jhu.edu 164 Etienne Hugues ehugues@buffalo.edu 109,134 Dongsung Huh dhuh@ucsd.edu 108 Alexander C Huk huk@mail.utexas.edu 177 Oliver Hulme o.hulme@ucl.ac.uk 135 Quentin JM Huys qhuys@gatsby.ucl.ac.uk 21, 82 Jaewon Hwang jhwang@bcs.rochester.edu 83 Michael R Ibbotson ibbotson@rsbs.anu.edu.au 127 Jennifer M Ichida jennifer.ichida@hsc.utah.edu 136 Masashi Inoue inou@ls.toyaku.ac.jp 143 Ramon Iovin riovin@bu.edu 15 Kazuya Ishibashi kazuya@mns.k.u-tokyo.ac.jp 115 Shin Ishii ishii@is.naist.jp 179 Toshiyuki Ishii tishii@brain.riken.jp 64 Laurent Itti itti@usc.edu 17,125 Vikram Jakkamsetti vxj037000@utdallas.edu 182 Santiago Jaramillo sjara@ieee.org 137 Vivek Jayaraman vivek@caltech.edu 65 Janneke FM Jehee jehee@cs.rochester.edu 66 Jose M Jerez jja@lcc.uma.es 57 Dezhe Z Jin djin@phys.psu.edu 67, 97 Jianzhong Jin jjin@mail.sunyopt.edu 48 Jorge V Jose jjosev@research.buffalo.edu 109 Kresimir Josic josic@math.uh.edu 195, 211 Jorge V José JJosev@research.buffalo.edu 134 Joseph K Jun juj12@psu.edu 97 Oliver Kacelnik ok@physiol.ox.ac.uk 98 Igor Kagan igor@vis.caltech.edu 234 Ryohei Kanzaki kanzaki@i.u-tokyo.ac.jp 150 Mimi H Kao mimi@phy.ucsf.edu 170 William L Kath kath@northwestern.edu 42 Yael Katz y-katz@northwestern.edu 42 Yakov Kazanovich yakov_k@impb.psn.ru 123 Georgios A Keliris georgios.keliris@tuebingen.mpg.de 26 Richard Kempter r.kempter@biologie.hu-berlin.de 118 Adam Kepecs kepecs@cshl.edu 84, 180 Peyman Khorsand pkhorsan@uci.edu 68 Michael P Kilgard kilgard@utdallas.edu 182 Sung S Kim sskim@jhu.edu 164 Eyal Y Kimchi eyal.kimchi@yale.edu 110 Andrew J King ajk@physiol.ox.ac.uk 98 Jennifer M. Klein jmklein@sas.upenn.edu 58 Ulf Knoblich knoblich@csail.mit.edu 116 Donald P Knudsen knudsen.d@neu.edu 109 Christof Koch koch@klab.caltech.edu 70, 131 Kristin Koch kochk@mail.med.upenn.edu 69

Randal A Koene randalk@bu.edu 99 Adam Kohn adamk@cns.nyu.edu 61 Minjoon Kouh kouh@mit.edu 116 John W Krakauer jkrakauer@neuro.columbia.edu 199 Alexander Kraskov kraskov@klab.caltech.edu 70 Gabriel Kreiman kreiman@mit.edu 116 Jens Kremkow Jen.Kremkow@incm.cnrs-mrs.fr 152 Nikolaus Kriegeskorte niko@nih.gov 138 P. S. Krishnaprasad krishna@isr.umd.edu 106 Jayant E Kulkarni jk2619@columbia.edu 71 Arvind Kumar arvind.kumar@biologie.uni-freiburg.de 72 Konrad Körding konrad@koerding.de 18,51,231 Darin R La Sota lasota.d@neu.edu 212 Giancarlo LaCamera lacamerag@mail.nih.gov 85, 89 Jonas Larsson jonas@cns.nyu.edu 139 Peter E. Latham pel@gatsby.ucl.ac.uk 47, 73, 75 Pak-Ming Lau plau@pitt.edu 92 Mark Laubach mark.laubach@yale.edu 110 Gilles Laurent laurentg@its.caltech.edu 65,111 Aurel A. Lazar aurel@ee.columbia.edu 74 Daeyeol Lee dlee@cvs.rochester.edu 28,83 Robert Legenstein legi@igi.tugraz.at 117 Christian Leibold c.leibold@biologie.hu-berlin.de 118 Mate Lengyel lmate@gatsby.ucl.ac.uk 7, 224 Peter Lennie 2, 240 Alexander Lerchner LerchnerA@mail.nih.gov 119 Nick A Lesica lesica@fas.harvard.edu 48 Liat Levita ll2250@columbia.edu 149 William B Levy wbl@virginia.edu 4 Michael S Lewicki lewicki@cnbc.cmu.edu 53 Meng-Ru Li mrli@phy.duke.edu 120 Timothy P Lillicrap tim@biomed.queensu.ca 102 Stephen G Lisberger sgl@phy.ucsf.edu 14, 197 Alan Litke alan.litke@cern.ch 238 Alan M Litke Alan.Litke@cern.ch 25, 162 Robert C Liu robert.liu@emory.edu 140 Yuan Liu yuanliu@Princeton.EDU 86 Zeng Liu bjr@ln.nimh.nih.gov 85 Yonatan Loewenstein yonatanl@mit.edu 87 Nikos K Logothetis nikos.logothetis@tuebingen.mpg.de 26 Mickey London m.london@ucl.ac.uk 73 Kenway Louie klouie@cns.nyu.edu 37 Jörg Lücke lucke@gatsby.ucl.ac.uk 100 Wei Ji Ma weijima@gmail.com 75 Weiji Ma weijima@bcs.rochester.edu 47 Wolfgang Maass maass@igi.tugraz.at 114, 117 Christian K Machens machens@cshl.edu 88 Anthony S. Maida maida@cacs.louisiana.edu 171 Zachary F Mainen zach@cshl.edu 81, 84, 166, 172, 180 Ania Majewska majewska@mit.edu 189 Guy Major gmajor@princeton.edu 107 Dominique Martinez Dominique.Martinez@loria.fr 44 Daniel Martí daniel.marti@upf.edu 80 Michael D Mauk Michael.D.Mauk@uth.tmc.edu 188 John Maunsell 29 David McAlpine d.mcalpine@ucl.ac.uk 76 James L McClelland jlm@cnbc.cmu.edu 181 Christin H McCool cdhansen@ucdavis.edu 141 Cameron McIntyre mcintyc@ccf.org 195 Judith McLean judy@retina.anatomy.upenn.edu 69 Markus Meister meister@fas.harvard.edu 154, 238 Bartlett W Mel mel@usc.edu 39, 191 Michael M Merzenich merz@phy.ucsf.edu 12 Nima Mesgarani mnima@umd.edu 77 Lee E Miller lm@northwestern.edu 157 Takafumi Minamimoto minamimotot@mail.nih.gov 89 Jude F Mitchell jude@salk.edu 142, 237 Sanjoy K Mitter mitter_removethisstring@mit.edu 163 Keiji Miura miura@ton.scphys.kyoto-u.ac.jp 78 Hiroyoshi Miyakawa miyakawa@ls.toyaku.ac.jp 143 Samat B Moldakarimov sam47@pitt.edu 181 Joseph D Monaco joe@neurotheory.columbia.edu 149 Christopher I Moore cim@mit.edu 227 Duncan Mortimer dmorti@gmail.com 41 Cynthia F Moss cmoss@psyc.umd.edu 34, 106 Ahmed A. Moustafa halimo19@hotmail.com 171 Anthony Movshon 1 J A Movshon movshon@nyu.edu 45

Eran A Mukamel emukamel@stanford.edu 190, 203 Paul W. Munro pmunro@mail.sis.pitt.edu 184 Isabel A Muzzio im128@columbia.edu 149 Farzan Nadim farzan@stg.rutgers.edu 151 Katherine Nagel knagel@phy.ucsf.edu 197, 220, 232 Shigehiro Namiki namiki@brain.imi.i.u-tokyo.ac.jp 150 Nandakumar S Narayanan kumar.narayana@yale.edu 110 Rama Natarajan rama@cs.toronto.edu 21,151 Vidhya Navalpakkam navalpak@usc.edu 17 Matthew J Nelson nelsonmj@vis.caltech.edu 30 William H Nesse nesse@math.utah.edu 204, 205 Leo Ng l.ng@utoronto.ca 210 Hendrikje Nienborg hn@lsr.nei.nih.gov 221 Fernando R Nodal fernando.nodal@physiol.ox.ac.uk 98 Crystal T Novitski novitski@utdallas.edu 182 Duane Q Nykamp nykamp@math.umn.edu 206 Donald M O'Malley d.omalley@neu.edu 109 Klaus Obermayer oby@cs.tu-berlin.de 192, 231 Shani Offen shani@cns.nyu.edu 222 Tatsuya Ohyama Tatsuya.Ohyama@uth.tmc.edu 188 Masato Okada okada@k.u-tokyo.ac.jp 78, 115, 143 Itsaso Olasagasti iolasaga@wellesley.edu 107 Cheryl A Olman caolman@umn.edu 223 Toshiaki Omori omori@mns.k.u-tokyo.ac.jp 143 Gergo Orban ogergo@sunserv.kfki.hu 224 Jake Ormond jake.ormond@utoronto.ca 178 Andrew M Oster oster@math.utah.edu 183 Gonzalo H Otazu otazu@cshl.edu 225 Hysell Oviedo oviedo@cshl.edu 46 Liam Paninski liam@stat.columbia.edu 71 Christopher Parisien cmparisi@uwaterloo.ca 112 Lucas C. Parra parra@ccny.cuny.edu 226 Carl H Parsons carl.parsons@newcastle.edu.au 98 Naama Parush naamap@alice.nc.huji.ac.il 207 Joseph J Paton jp2063@columbia.edu 91 Barak A. Pearlmutter barak@cs.nuim.ie 137, 226 Yefei Peng ypeng@mail.sis.pitt.edu 184 Claudia A Perez andiraperez@hotmail.com 182 Laurent U Perrinet Laurent.Perrinet@incm.cnrs-mrs.fr 152 Matthew S Perry bark5949@yahoo.com 182 Bijan Pesaran bijan@nyu.edu 30 Michael J Pesavento michael_pesavento@urmc.rochester.edu 144 Dumitru Petrusca Dumitru.Petrusca@cern.ch 25, 162 Jean-Pascal Pfister jean-pascal.pfister@epfl.ch 185 Jonathan W Pillow pillow@gatsby.ucl.ac.uk 153 David J Pinto david_pinto@urmc.rochester.edu 144 Xaq Pitkow pitkow@fas.harvard.edu 154 Dietmar Plenz plenzd@mail.nih.gov 214 Tomaso Poggio tp@ai.mit.edu 116 Alon Polsky alonpol@techunix.technion.ac.il 39 Alexandre Pouget alex@bcs.rochester.edu 47, 75 Pierre Pouget pierre.pouget@vanderbilt.edu 128 Steven A Prescott sprescott@salk.edu 145 Nicholas SC Price nicholas.price@anu.edu.au 127 Simon Prince s.prince@cs.ucl.ac.uk 135 Astrid A Prinz astrid.prinz@emory.edu 208 Dominique L Pritchett pritched@mit.edu 85 Amanda C Puckett apuckett@utdallas.edu 182 Gopathy Purushothaman gopathy@uchicago.edu 242 Steven R Quartz steve@hss.caltech.edu 121 Rodrigo Quian Quiroga rodri@vis.caltech.edu 70 Ithai Rabinowitch ithai@lobster.ls.huji.ac.il 9 Marcus Raichle 33 Sachin P Ranade ranades@cshl.edu 172 Chenchal Rao jeffrey.d.schall@vanderbilt.edu 128 Charles P Ratliff dutch@retina.anatomy.upenn.edu 58, 155 Stéphanie Ratté stephanie.ratte@mail.mcgill.ca 145 Jenny Read J.C.A.Read@ncl.ac.uk 156 James Rebesco j-rebesco@northwestern.edu 157 Pamela Reinagel preinagel@ucsd.edu 158 Alexandre Reynaud Alexandre.Reynaud@incm.cnrs-mrs.fr 152 John H Reynolds reynolds@salk.edu 142, 237 Barry J Richmond bjr@ln.nimh.nih.gov 85, 89 Hiroshi Riquimaroux hrikimar@mail.doshisha.ac.jp 35 Jason Ritt jritt@mit.edu 227 Carson B Roberts carson_roberts@yahoo.com 146 Robert Rohrkemper rohrkemper@gmail.com 228 Edmund T Rolls Edmund.Rolls@psy.ox.ac.uk 57

Ranulfo Romo romo@ifc.unam.mx 88 Andrew F Rossi andrew.rossi@vanderbilt.edu 128 Arnd Roth arnd.roth@ucl.ac.uk 73 Constantin A Rothkopf crothkopf@cvs.rochester.edu 173 Stefan Rotter rotter@biologie.uni-freiburg.de 72 Yasser Roudi yasser@gatsby.ucl.ac.uk 209 Alexander Roxin alexander.roxin@upf.edu 80 Jonathan E Rubin rubin@math.pitt.edu 195 Michele Rucci rucci@cns.bu.edu 15 William S Ryu wsryu@princeton.edu 198 Maneesh Sahani maneesh@gatsby.ucl.ac.uk 243 Paul Sajda ps629@columbia.edu 229 Yutaka Sakai sakai@inter7.jp 174 Emilio Salinas esalinas@wfubmc.edu 159 Daniel Salzman cds2005@columbia.edu 91 Aravinthan Samuel samuel@physics.harvard.edu 105, 196 Fernanda Saraga fernanda.saraga@utoronto.ca 210 Massimo Scanziani 13 Jeff Schall 27 Jeffrey D Schall jeffrey.d.schall@vanderbilt.edu 128 Jackie Schiller jackie@techunix.technion.ac.il 39 Denis Schluppeck denis@cns.nyu.edu 222 Robert Schmidt r.schmidt@biologie.hu-berlin.de 118 Dietmar Schmitz dietmar.schmitz@charite.de 118 Mark J Schnitzer mschnitz@stanford.edu 190, 203 Jan WH Schnupp jan.schnupp@physiol.ox.ac.uk 230 David Schoppik junk@schoppik.com 197 Christoph E Schreiner chris@phy.ucsf.edu 12, 140 James Schummers schummej@mit.edu 231 Lars Schwabe schwabe@cs.tu-berlin.de 136 Idan Segev idan@lobster.ls.huji.ac.il 9 Ronen Segev RSegev@molbio.Princeton.EDU 69 Eyal Seidemann eyal@mail.cps.utexas.edu 126 Terrence J Sejnowski terry@salk.edu 145, 147, 199 Walter Senn wsenn@cns.unibe.ch 187 Thomas Serre serre@ai.mit.edu 116 Sebastian H Seung seung@mit.edu 23, 87, 186 Ben Seymour bseymour@fil.ion.ucl.ac.uk 175 Michael N Shadlen shadlen@u.washington.edu 177 Reza Shadmehr reza@bme.jhu.edu 18 Maoz Shamir shamir@bu.edu 160 Shihab Shamma sas@umd.edu 77 Ladan Shams ladan@psych.ucla.edu 121 Jitendra Sharma jeetu@mit.edu 231 Tatyana Sharpee sharpee@phy.ucsf.edu 31, 220, 232 Jonathan Shaw jshaw@cs.rochester.edu 161 Alexander Sher sasha@scipp.ucsc.edu 25, 162 Denis Sheynikhovich denis.sheynikhovich@epfl.ch 50 LieJune Shiau shiau@cl.uh.edu 211 Yury P Shimansky yury.shimansky@asu.edu 94 Stuart Shipp s.shipp@ucl.ac.uk 135 Jonathon Shlens shlens@salk.edu 162 Athanassios Siapas siapas@caltech.edu 238 Thanos G Siapas thanos@caltech.edu 26 Hava Siegelmann hava@cs.umass.edu 228 Eero P Simoncelli eero@cns.nyu.edu 153, 236 Tania Singer t.singer@fil.ion.ucl.ac.uk 175 Frances K Skinner fskinner@uhnresearch.ca 210 Stelios M Smirnakis stelios.smirnakis@tuebingen.mpg.de 26 Evan C Smith evan@cnbc.cmu.edu 233 Max Snodderly msnodderly@mcg.edu 234 Sara A Solla solla@northwestern.edu 157 Samuel G Solomon samuels@medsci.usyd.edu.au 240 Haim Sompolinsky 10, 96, 154, 186, 218 Edward Soucy soucy@mcb.harvard.edu 238 Martin A Spacek mspacek@interchange.ubc.ca 235 Nelson Spruston spruston@northwestern.edu 42 Lakshminarayan Srinivasan ls2@neurostat.mgh.harvard.edu 163 Arun P Sripati sparun@cnbc.cmu.edu 122 Garrett B Stanley gstanley@deas.harvard.edu 48, 216 Armen Stepanyants a.stepanya@neu.edu 212 Greg J Stephens gstephen@princeton.edu 198 Peter Sterling peter@retina.anatomy.upenn.edu 58, 69, 155 Klaus M. Stiefel stiefel@salk.edu 147 Alan A Stocker alan.stocker@nyu.edu 236 Mark Stopfer stopferm@mail.nih.gov 111 Thomas Strosslin thomas.strosslin@a3.epfl.ch 50 Kristy A Sundberg sundberg@salk.edu 237

Mriganka Sur msur@mit.edu 189, 231 Kelly J Suter ksuter@LearnLink.Emory.Edu 146 Karel Svoboda 8 Nicholas V Swindale swindale@interchange.ubc.ca 235 Tobi Szuts szuts@fas.harvard.edu 238 Lung-Hao Tai ltai@cshl.edu 239 Chris Tailby ct@cns.nyu.edu 240 Hirokazu Tanaka hirokazu@salk.edui 199 David W Tank dwtank@princeton.edu 107 Jordan A. Taylor jat4@cec.wustl.edu 103 Josh B Tenenbaum jbt@mit.edu 18 Jun-nosuke Teramae teramae@brain.riken.jp 213 David Terman terman@math.ohio-state.edu 195 Pramodsingh H Thakur pramod@jhu.edu 164 Tara C Thiagarajan tarat@mail.nih.gov 214 Kurt A. Thoroughman thoroughman@biomed.wustl.edu 103 Kay Thurley k.thurley@biologie.hu-berlin.de 118 Paul H Tiesinga tiesinga@physics.unc.edu 134, 241 Naftali Tishby tishby@cs.huji.ac.il 207 Anne-Elise Tobin atobin@brandeis.edu 148 Emanuel Todorov todorov@ucsd.edu 16, 108 Andreas S Tolias andreas.tolias@tuebingen.mpg.de 26 Susumu Tonegawa tonegawa@mit.edu 189 Elizabeth B Torres etorres@vis.caltech.edu 200 Taro Toyoizumi taro@sat.t.u-tokyo.ac.jp 165 Alessandro Treves ale@sissa.it 209 Bryan P Tripp bptripp@engmail.uwaterloo.ca 215 Wilson Truccolo Wilson_Truccolo@Brown.edu 201 Srinivas C Turaga sturaga@mit.edu 186 Douglas B Tweed douglas.tweed@utoronto.ca 102, 104, 202 Naoshige Uchida uchida@cshl.edu 84, 166, 180 Leslie Ungerleider 38 Martin Usrey wmusrey@ucdavis.edu 54 Eleni Vasilaki vasilaki@cns.unibe.ch 187 Jonathan D Victor jdvicto@med.cornell.edu 59, 167 Renan Vitral renan@icb.ufjf.br 176 Horatiu Voicu horatiu@voicu.us 188 Michael T Wahl mwahl@berkeley.edu 168 Pascal Wallisch wallisch@uchicago.edu 242 Kenneth Whang 20 Kuan Hong Wang wangkh@mit.edu 189 Xiao-Jing Wang xjwang@brandeis.edu 79,177,187 Yoshiaki Watanabe kwatanab@mail.doshisha.ac.jp 35 Roxanna M Webber webber@fas.harvard.edu 216 Mike Wehr wehr@uoregon.edu 217 Chong Weng cweng@sunyopt.edu 48 Daniel Z Wetmore wetmore@stanford.edu 190 Theodore G Weyand tweyan@lsuhsc.edu 54 Olivia L White white.olivia@gmail.com 218 Louise Whiteley l.whiteley@ucl.ac.uk 243 Jim Wielarrd djw21@columbia.edu 229 Charles J Wilson charles.wilson@utsa.edu 40 Robert C Wilson rcwilson@seas.upenn.edu 169 Klaus Wimmer klaus@cs.tu-berlin.de 231 Kong-Fatt Wong kfwong@brandeis.edu 177 Melanie A Woodin mwoodin@zoo.utoronto.ca 178 Brian D Wright bdwright@phy.ucsf.edu 170 Xundong Wu xundongw@usc.edu 191 Zhiyong Yang yang@mail.cps.utexas.edu 126 Chun-I Yeh cyeh@sunyopt.edu 48 Wako Yoshida wako-y@is.naist.jp 179 Eric D Young eyoung@bme.jhu.edu 49 Joshua M Young josh@cs.tu-berlin.de 192 Angela J Yu ajyu@princeton.edu 244 Anthony M Zador zador@cshl.edu 46, 133, 225, 239 Hatim A Zariwala zariwala@cshl.edu 180 Riccardo Zecchina zecchina@ictp.it 193 Richard Zemel zemel@cs.toronto.edu 21 Kechen Zhang kzhang4@jhem.jhmi.edu 6 Tao Zhang tzhang@ucdavis.edu 245 Junmei Zhu junmeizhu@gmail.com 219 Leo van Hemmen lvh@tum.de 194

Cosyne Workshops

The Canyons Park City, Utah

Thursday, March 9

7:30 – 8:30 AM Full Breakfast Kokopelli Parlor II 8:30 – 11:30 AM Workshops Parlor rooms (see below) Hot and cold beverages – Grand Ballroom Lobby 4:30 – 7:30 PM Workshops continue Parlor rooms (see below) Hot and cold beverages – Grand Ballroom Lobby Functional architectures and neuronal computations in the prefrontal cortex Organized by: Etienne Koechlin, Gregor Rainer, and Xiao-Jing Wang Kokopelli Parlor III Models of multisensory integration: psychophysical and neural constraints Organized by: Virginie van Wassenhove, Ladan Shams, and John Jeka White Pine Parlor I Adaptation: neural, psychological, and computational aspects Organized by: Odelia Schwartz, Colin Clifford, and Peter Dayan Arrowhead Parlor II The next generation of fMRI: Statistical learning and the complexity of real life Organized by: David Heeger White Pine Parlor II The computational songbird. Perception, generation and learning of complex temporal sequences: experiments meet theory Painted Horse II Organized by: Kamal Sen Models of model systems Organized by: Anne-Elise Tobin and Adam Taylor Arrowhead Parlor I Advances in activity-dependent plasticity. Organized by: Paul Munro Kokopelli Parlor I

Friday, March 10

7:30 – 8:30 AM Full Breakfast Kokopelli Parlor II 8:30 – 11:30 AM Workshops Parlor rooms (see below) Hot and cold beverages – Grand Ballroom Lobby 4:30 – 7:30 PM Workshops continue Parlor rooms (see below) Hot and cold beverages – Grand Ballroom Lobby 8:00 – 11:30 PM Banquet Kokopelli Grand Ballroom Functional architectures and neuronal computations in the prefrontal cortex (continues) Organized by: Etienne Koechlin, Gregor Rainer, and Xiao-Jing Wang Kokopelli Parlor III Difficult issues in auditory scene analysis Organized by: Barbara Shinn-Cunningham and Shihab Shamma White Pine Parlor I Neural and behavioral variability: nuisance or necessity? Organized by: Leslie Osborne and Philip Sabes Arrowhead Parlor I Computing with spikes: more than spike-counts - every spike counts? Sophie Deneve, Boris Gutkin, and Mate Lengyel Arrowhead Parlor II The role of natural images in guiding our understanding of visual function Organized by: Nicole Rust, Jonathan Pillow, and Eero Simoncelli Kokopelli Parlor I Genetic approaches for system neuroscience Organized by: Gero Miesenboeck and Susana Lima Painted Horse II Parietal cortex: function and computations Organized by: Jennifer Groh White Pine Parlor II

Functional architectures and neuronal computations in the prefrontal cortex Etienne Koechlin1, Gregor Rainer2, and Xiao-Jing Wang3

1Pierre et Marie Curie University, 2Max-Planck-Institute, Tuebingen, 3Brandeis University

Abstract A great challenge in current neuroscience is to understand how the prefrontal cortex subserves the temporal and hierarchical organization of goal-directed behaviors. This workshop aims to discuss recent progress in experimental studies and computational modeling that have begun to identify general principles and key open questions concerning the prefrontal functions, their cellular and microcircuit bases. Important advances have been recently made at the functional, network and cellular levels and the workshop will bring together leading investigators from various fields including neuro-anatomy, neurophysiology, functional imaging and modeling, who have actively contributed to those recent progress. The workshop will focus on the integration of those multiple levels to better characterize information processing in the prefrontal cortex underlying working memory, executive control, decision-making and to clarify the relations between those basic functions. Main topics to be presented by speakers and discussed among participants will include: Biophysical mechanisms, neuronal coding, local network dynamics and functional architectures in the prefrontal cortex involved in integrating information from temporally dispersed events and in processing hierarchical structures of action plans in relation with expected rewards. Our aim is to especially encourage interactions between experimentalists and theoreticians to discuss emerging ideas, concepts and models that will help the field to move forward.

Schedule (Thursday)

8:30 - 9:00 Helen Barbas (Boston University) Specialization and synergism of prefrontal pathways for cognition, emotion and action

9:10 - 9:40 Etienne Koechlin (Pierre et Marie Curie University, Paris)

Temporal and hierarchical dimensions of executive control in the human prefrontal cortex

9:50 - 10:10 Break

10:10 - 10:40 John O'Doherty (Caltech) Abstract state-based inference in human ventromedial prefrontal cortex during reward-based decision making

10:50 - 11:20 Gustavo Deco (University of Barcelona) The role of fluctuations in decision-making

4:30 - 5:00 Matthew Rushworth (Oxford) Contrasting the roles of the medial and lateral prefrontal cortices in decision-making

5:10 - 5:40 Michael Colombo (Dunedin) Neural correlates of executive control in the avian "prefrontal cortex 5:50 - 6:10 Break 6:10 - 6:40 Nicolas Brunel (Paris) Scenarios for persistent activity in cortical network models

6:50 - 7:30 Kenji Doya (Okinawa Institute of Science and Technology) Short- and long-term reward prediction in cortico-basal ganglia loops

Gaps in the schedule are for questions/discussion.

Schedule continues (Friday)

8:30 - 9:00 Daeyeol Lee (Rochester) Reinforcement learning and decision making in prefrontal cortex

9:10 - 9:40 Gregor Rainer (Max-Planck-Institute, Tuebingen) Cooperation between prefrontal and visual cortex during working memory

9:50 - 10:10 Break 10:10 - 10:40 Aldo Genovesio (NIH) Representation of strategies and goals in the prefrontal cortex

10:50 - 11:20 Stefano Fusi (University of Bern)

A neural model of flexible sensori-motor mapping: learning and forgetting on multiple timescales

4:30 - 5:00 Xiao-Jing Wang (Brandeis University) Slow reverberatory cortical dynamics underlying cognition

5:10 - 5:40 Shintaro Funahashi (Kyoto University)

Neural mechanisms of spatial working memory: contributions of the dorsolateral prefrontal cortex and the orbitofrontal cortex

5:50 - 6:10 Break

6:10 - 6:40 Guillermo Gonzalez-Burgos (Pittsburgh)

Synaptic and electrical signaling and dopamine neuromodulation in microcircuits of the monkey dorsolateral prefrontal cortex

6:50 - 7:30 Min-Whan Jung (Suwon) Learning and memory in the prefrontal cortex

Models of multisensory integration: psychophysical and neural constraints Virginie van Wassenhove1,2, Ladan Shams1, and John Jeka3

1UCLA, 2Caltech, 3University of Maryland College Park

Abstract This workshop will bring together researchers to explore current issues in multisensory integration at both the neural and psychophysical level. The workshop will address shortcomings in current models of multisensory integration, and emphasize identification of key properties for future models.

Current models of multisensory integration have not systematically incorporated the spatial and temporal factors observed in human psychophysics. For instance, the relative timing between stimuli is an important factor for the degree of interaction between the sensory modalities, and between the sensory modalities and the motor system. Yet, timing has been largely neglected by the current models of crossmodal integration. Additionally, attention to one modality vs. another can modulate the outcome of multisensory integration considerably, and models of cue combination do not currently account for these attentional factors. These and several other open questions such as the following will be discussed.

• What are the dynamics of multisensory integration? How can the temporal and spatial concordance factors be incorporated into current models of multisensory integration?

• Can convergence onto multisensory neurons suffice to account for sensori-motor integration and multisensory perceptual phenomena? What are the factors and computations involved in binding the signals from different modalities?

• At which representational stage is information from different modalities integrated? How can we account for attentional modulation?

Schedule (Thursday)

8:30 - 8:35 Opening remarks

8:35 - 9:00 Sean Carver, Tim Kiemel, and John Jeka (University of Maryland, College Park)

Adaptive multisensory fusion: resolving sensory conflicts in an uncertain changing environment

9:10 - 9:35 Dora Angelaki (Washington University School of Medicine)

Multisensory integration in MSTd: Reference frames and correlations with behavior

9:45 - 10:10 Break 10:10 - 10:35 Phillip Sabes (UCSF) See, feel, and learn: sensory integration and adaptation

10:45 - 11:05 Virginie van Wassenhove (UCLA, Caltech) Analysis-by- synthesis: hearing and seeing speech that is being produced

11:15 - 11:30 Discussion

4:30 - 4:55 Alex Pouget (University of Rochester) Neural basis of Bayes-optimal mutlisensory integration: theory and experiments

5:05 - 5:30 Tom Anastasio (University of Illinois) Testing Models of Multisensory Integration 5:30 - 6:00 Break

6:00 - 6:25 Ladan Shams (UCLA) and Wei-Ji Ma (University of Rochester)

Bayesian inference as a unifying model for auditory-visual integration-segregation

6:35 - 7:30 Concluding Remarks and Discussion Gaps in the schedule are for questions/discussion.

Adaptation: neural, psychological, and computational aspects Odelia Schwartz1, Colin Clifford2, and Peter Dayan3

1Salk Institute, 2University of Sydney, 3Gatsby Computational Neuroscience Unit

Abstract Adaptation is ubiquitous in neurons and in perception. The perceptual phenomenon, in the form of bias and sensitivity after-effects, has intrigued scholars at least since the time of Aristotle. However, the functional goals of neural and psychological adaptation remain elusive. New physiological and psychophysical data, along with emerging statistical and computational models, make this an opportune time to bring together experimentalists and theoreticians.

In this workshop, we will discuss recent data; contrasting theories about adaptation; and consider how the interplay between experiment and theory could lead to informative future directions.

Issues will include, but are not limited to:

• Experiments: what changes along hierarchical sensory pathways in response to adapting stimuli; to what attributes of which stimuli do we adapt; to what aspects of artificial and natural statistical distributions of stimuli do we adapt, and how.

• Theory: functional modeling approaches ranging from efficient coding; other forms of unsupervised learning; re-calibration; invariant representations; and Bayesian frameworks.

• Interplay: how can current data constrain and test our functional understanding; what new experiments might be revealing.

Schedule (Thursday)

8:30 - 9:00 Odelia Schwartz (Salk Institute) Adaptation and natural image statistics 9:10 - 9:40 Markus Meister (Harvard University) Neural mechanisms for predictive coding in the retina

9:50 - 10:10 Break

10:10 - 10:40 Adam Kohn (Albert Einstein College of Medicine and New York University)

Adaptation to mean and variance in the primate visual system

10:50 - 11:20 Adrienne Fairhall (University of Washington) Adaptation in cortical neurons

4:30 - 5:00 Colin Clifford (The University of Sydney) Self-calibration in sensory coding

5:10 - 5:40 Garrett Stanley (Harvard University) Adaptive mechanisms for enhancing neural encoding in sensory pathways

5:50 - 6:10 Break

6:10 - 6:40 Alan Stocker (New York University) Adaptation within a Bayesian framework for perception

6:50 - 7:20 Michael Webster (University of Nevada) Norms, novelty, and the consequences of adaptation for perception

The next generation of fMRI: statistical learning and the complexity of real life David Heeger

Abstract Recent work with functional magnetic resonance imaging (fMRI) is pushing simultaneously in two different directions. The first is to explore the function and organization of the human brain under natural and unbounded settings. The empirical protocol involves measuring brain activity with fMRI during free viewing of an engaging sensory and emotional experience. Because conventional analysis methods are unsuitable for such an open-ended experiment, new methods have been applied that do not rely on a preconceived notion of what to expect for the outcome. The simplest of these approaches has been to utilize inter-subject correlations as a means for extracting the dimensions of complex stimuli to which particular brain areas are responsive. More sophisticated versions of this approach have used unsupervised learning techniques (e.g., independent component analysis) to extract "features" (spatial patterns and time courses) of brain activity. The second direction has been to apply supervised learning techniques (e.g., classifiers such as support vector machines and Fisher linear discriminators) for discovering distributed patterns of brain activity that are predictive of perceptual state. These new approaches to brain imaging have attracted considerable attention. The goal of the proposed workshop is to bring together a group of neuroscientists who have been developing and using these methods, to discuss their potential and their limitations, and to consider novel applications of these methods.

Schedule (Thursday)

8:30 - 9:00 David Heeger (NYU) The next generation of functional magnetic resonance imaging

9:00 - 9:30 Yukiyasu Kamitani (ATR, Japan)

Decoding human visual perception: Ensemble feature selectivity and a method for mind-reading

9:45 - 10:00 Break

10:00 - 10:30 John-Dylan Haynes (Max-Planck-Institute, Leipzig)

Decoding conscious and unconscious perception from dynamic brain patterns

10:45 - 11:15 Justin Gardner (NYU) High spatial resolution imaging to determine the dependence on spatial scale of classifier performance

4:30 - 5:00 Jim Haxby (Princeton University) Analysis of multi-voxel patterns of response to faces and objects

5:15 - 5:45 Kalanit Grill-Spector (Stanford)

High-resolution fMRI reveals heterogeneous fine-scale structure in human face-selective cortex

6:00 - 6:15 Break

6:15 - 6:45 Uri Hasson (NYU) Inter-subject correlation analysis as a new tool for studying the brain during free viewing of dynamic natural scenes

7:00 - 7:30 General Discussion

The computational songbird. Perception, generation and learning of complex temporal sequences: experiments meet theory

Kamal Sen Boston University

Abstract The perception, generation and learning of temporal sequences is an active and exciting area of current theoretical research. An attractive model system for investigating these problems experimentally is the birdsong system. This workshop will bring together theorists working on perception, generation and learning of temporal sequences, with birdsong experimentalists investigating these problems in circuits in the songbird brain, with the goal of generating cross-talk between theorists and experimentalists, and identifying future directions that could benefit from synergistic approaches.

Schedule (Thursday)

8:30 - 8:45 Introduction

8:45 - 9:05 Kamal Sen (Boston University) Neural discrimination of birdsongs in field L

9:15 - 9:35 Tim Gentner (UCSD) Mechanisms for the representation of pattern-embedded auditory objects 9:45 - 10:15 Break

10:15 - 10:35 Haim Sompolinsky (Hebrew University) Discriminating temporal patterns: spiking neurons and 'ideal observers'

10:45 - 11:05 Maoz Shamir (Boston University) Temporal coding of time varying stimuli

4:30 - 4:50 Dan Margoliash (University of Chicago) State dependent mechanisms of song learning

5:00 - 5:20 Michael Brainard (UCSF) Contributions of avian basal ganglia circuitry to adult song plasticity 5:30 - 6:00 Break

6:00 - 6:20 Sebastian Seung (MIT) Intrinsic bursting enhances the robustness of a neural network model of sequence generation by avian brain area HVC

Models of model systems Anne-Elise Tobin and Adam Taylor

Brandeis University

Abstract Why would you care how a lobster eats, as long as you can eat it? Why care how a grasshopper hears, how a sea slug feeds, how a worm turns, or how a fly sees? If a fundamental goal of neuroscience is to understand how our brains work, why study animals whose most recent common ancestor with humans lived some 500 million years ago? This workshop will draw together people who presumably have some answer to these questions. It will focus on fundamental questions that are relevant to all nervous systems but can be addressed more directly in the "simple" nervous systems of invertebrates. These systems typically have a (relatively) small number of neurons, making them ideal for computational studies, and eliminating the need to model only a representative sample of a neuronal population. Furthermore, most invertebrate neurons are identifiable from animal to animal, and in the best cases they offer simultaneous access to behavior and to sensory neurons, motor neurons, and interneurons. Taken together, these features make invertebrate nervous systems uniquely suited for revealing the fundamental toolkit used by all nervous systems to compute behavior. Many phenomena that were first dismissed as "weird invertebrate things" later turned out to be ubiquitous, such as the fast transient potassium current, endogenously bursting neurons, neuromodulation, gap junctions, and neuronal homeostasis. The invited speakers will present a sampling of current work on modeling of model systems.

Schedule (Thursday)

8:45 - 9:20 Rob de Ruyter van Steveninck (Indiana University, Bloomington)

Motion estimation in the blowfly: a testbed for theories of optimal computation

9:30 -10:05 Vladimir Brezina (Mount Sinai School of Medicine) Modeling neuromuscular modulation in Aplysia

10:15 - 10:35 Break

10:35 - 11:10 Shawn Lockery (University of Oregon) As the worm turns: stochastic models of the neural network for chemotaxis in the nematode C. elegans

4:40 - 5:15 Andreas Herz (Humboldt University) Auditory processing of acoustic communication signals: from biophysics to biological function

5:25 - 6:00 Astrid Prinz (Emory University) Lessons about neural system robustness and homeostasis from the crustacean stomatogastric ganglion

6:10 - 6:30 Break

6:30 - 7:05 Axel Borst (Max Planck Institute of Neurobiology, Martinsried) Neural processing of optic flow in the fly

Advances in activity-dependent plasticity Paul Munro

University of Pittsburgh

Abstract In this workshop we propose to expand the theme to include both spike- dependent and rate-dependent models. Again, we will discuss both laboratory data and theoretical approaches. While the mathematical and cognitive aspects of rate-based Hebb-like rules have been broadly explored, the computational implications of STDP are not as well understood. Hebbian learning in neural networks requires both correlation-based synaptic plasticity and a mechanism that induces competition between different synapses. Spike-timing-dependent synaptic plasticity is especially interesting because it combines both of these elements in a single synaptic modification rule. Some recent work has examined the possibility that STDP may underlie older models, such as the BCM rule. The change in synaptic efficacy arising from STDP is highly sensitive to temporal correlations between different presynaptic spike trains. Furthermore, it can generate asymmetric and directionally selective receptive fields, a result supported by experiments on experience-dependent modifications of hippocampal place fields. Finally, spike-timing-dependent plasticity automatically balances excitation and inhibition producing a state in which neuronal responses are rapid but highly variable. The major goals of the workshop are:

• Review current experimental results on spike-timing-dependent synaptic plasticity and related effects. • Discuss models and mechanisms for this form of synaptic plasticity. • Explore the relationship of STDP with other approaches. • Reconcile the rate-based and spike-based plasticity data with a unified theoretical framework (very optimistic!).

Schedule (Thursday)

8:30 - 8:45 Welcome

8:45 - 9:05 Walter Senn (University of Berne)

Learning in the presence of bounded synapses: emergence of balanced neurons and equalized synaptic strengths

9:10 - 9:30 Mayank Mehta (Brown University) Novel features of synaptic plasticity induced by natural spike patterns

9:35 - 9:50 Break

9:50 - 10:10 Paul Munro (University of Pittsburgh) External application of STDP: a computer simulation

10:15 - 10:35 Guoqiang Bi (University of Pittsburgh)

Modular competition driven by nmda receptor subtypes in spike-timing-dependent plasticity

4:30 - 4:50 Melanie Woodin (University of Toronto)

Modular competition driven by nmda receptor subtypes in spike-timing-dependent plasticity

4:55 - 5:15 Guy Billings (University of Edinburgh)

Equilibrium fluctuations and receptive field characteristics in weight-dependent and non weight-dependent plastic spiking networks

5:20 - 5:45 Break

5:45 - 6:05 Jean-Pascal Pfister (EPFL)

Beyond pair-based STDP: a phenomenological rule for spike triplet and frequency effects

6:10 - 6:30 Rob Froemke (UCSF) Multi-spike interactions in spike-timing-dependent plasticity 6:35 - 7:30 Discussion/Wrapup

Difficult issues in auditory scene analysis Barbara Shinn-Cunningham1 and Shihab Shamma2

1Boston University, 2University of Maryland College Park

Abstract This workshop will focus on four issues important to Auditory Scene Analysis (ASA), specifically, the role, interactions, and effects of the following on ASA: 1) attention, 2) informational masking, 3) spatial and binaural hearing, and 4) uncertainty and bistability. The goal of the workshop is to bring together many of the senior as well as more junior researchers in the area to engage in active discussion and exploration of these topics. The non-traditional format will consist of a number of short presentations designed to provoke and encourage discussion.

Schedule (Friday)

8:30 - 8:40 Wake up and Introduction Announcement

Introduction, Extreme Position Statement

General Griping

8:40 - 8:55 Rhodri Cusack (MRC, Cambridge) Attention and source segregation 9:00 - 9:15 Erv Hafter (UC Berkeley) Point

9:20 - 10:00 Elyse Sussman (Albert Einstein College of Medicine, Yeshiva University) Counterpoint

Cindy Moss (University of Maryland College Park) Discussion

10:00 - 10:30 Break 10:30 - 10:45 Daniel Pressnitzer (Ecole Normale Superieure) Bistability and source segregation 10:50 - 11:05 Sue Denham (University of Plymouth) Point and Counterpoint 11:10 - 11:30 Christophe Micheyl (MIT) Discussion

Pierre Divenyi (EBIRE, Martinez)

4:30 - 4:45 Steve Colburn (Boston University) Spatial hearing and source segregation

4:50 - 5:05 Chris Darwin (University of Sussex) Point 5:10 - 5:45 Rich Stern (Carnegie Mellon University) Counterpoint

David McAlpine (UCL) Discussion

5:45 - 6:15 Break

6:15 - 6:30 Bill Yost (Parmly Hearing Institute, Loyola University) Information masking and source segregation

6:35 - 6:50 Andy Oxenham (University of Minnesota) Point 6:55 - 7:30 Ginny Richards (University of Pennsylvania) Counterpoint

Doug Brungart (Air Force Research Lab) Discussion

Neural and Behavioral variability: nuisance or necessity? Leslie Osborne and Philip Sabes

Abstract As every psychophysicist knows, behavior and perception are variable. Although sensory signals can be reliably represented in the firing patterns of neurons, often with precision that is nearly optimal given the fidelity of sensory transduction, the same sensory stimuli result in quite variable percepts and behavioral responses. Given our current state of knowledge about the cellular and synaptic properties of neurons, it is reasonable to believe that neural computation itself is a source of noise. However neurobiologists have been arguing for years, often with great passion, over the degree of "noisiness" of single neurons and neuronal populations. Fortunately, as more neurophysiologists connect neural activity to trial-by-trial behavior, the origins and the import of neuronal variability will increasingly become a topic of research.

Schedule (Friday)

8:30 - 8:45 Opening remarks

8:45 - 9:05 Rob de Ruyter von Steveninck (Indiana University, Bloomington) External noise, internal noise and neural computation

9:15 - 9:35 Mike DeWeese (CSHL) How much cortical variability is really noise? 9:45 - 10:15 Break

10:15 - 10:35 Leslie Osborne (UCSF) Is behavior as noisy as you think it is?

10:45 - 11:05 Emo Todorov (UCSD) Evidence for motor noise minimization in cortical representations, muscle activation patterns, and movement trajectories

4:30 - 4:50 Peter Latham (Gatsby Computational Neuroscience Unit, UCL)

Intrinsic variability in cortical networks: it's here to stay and there's nothing we can do about it.

5:30 - 6:00 Break

6:00 - 6:20 Alexandre Pouget (University of Rochester) Random thoughts on the source and role of cortical variability

6:30 - 6:50 Philip Sabes (UCSF) Variability, coordinate transforms, and learning

7:00 - 7:30 Discussion, Debate, Wrap-up, Heavy drinking

Computing with spikes: more than spike-counts - every spike counts? Sophie Deneve1, Boris Gutkin2, and Mate Lengyel3

1CNRS Lyon, 2CNRS and the Pasteur Institute, 3Gatsby Computational Neuroscience Unit

Abstract There is growing evidence in a number of cortical areas that individual spikes, beyond just gross firing rates averaged over hundreds of milliseconds, play a central role in neural information processing. However, it is not yet well characterized what computations may be supported by network dynamics in which spikes count beyond the "spike-counts". In this workshop we bring together a variety of theoretical as well as experimental approaches, ranging from dynamical systems to Bayesian inference and from in vitro to in vivo recordings, and explore how spiking-based dynamics can be useful for performing a number of neurobiologically relevant computations.

Schedule (Friday)

8:50 - 9:20 Richard Zemel (University of Toronto) Recursive Bayesian estimation in population codes

9:30 - 10:00 Break

10:00 - 10:30 Yves Fregnac (CNRS) Time precision of the spiking behaviour of V1 cortical neurons is optimally adapted to the complexity of natural visual scenes

10:40 - 11:20 Peter Latham (Gatsby Computational Neuroscience Unit, UCL)

Requiem for a spike

4:30 - 5:00 Sophie Deneve (Neural Theory Group, ENS Paris) Spikes propagating beliefs

5:10 - 5:40 Break

5:40 - 6:10 Seb Wills (University of Cambridge) Distributed Phase Codes

The role of natural images in guiding our understanding of visual function Nicole Rust1, Jonathan Pillow2, and Eero Simoncelli1

1NYU, 1Gatsby Computational Neuroscience Unit

Abstract At all levels of visual processing, natural images are being used both to provide motivation for theories, and as a source of experimental stimuli. But the extent of their utility is also controversial. From an experimental perspective, some argue that simple, synthetic stimuli (such as bars, points of light and gratings) have failed to capture fundamental properties of visual neurons that are only activated under naturalistic stimulation conditions. Others counter that our current poor understanding of natural image properties renders them nearly useless for developing hypothesis driven research. And staking out the middle ground are those who argue that natural scenes play an important albeit limited role, such as for initial exploration of little-understood visual areas, or as the ultimate validation test for a model of neural response. On the theoretical side, some have attempted to construct models of visual function that are optimal in terms of evolutionary pressures, while others argue that these approaches are futile because the cost functions that the visual system seeks to minimize are unknown. The purpose of this workshop is to explore these different viewpoints in an attempt to arrive at a better understanding of these complex issues.

Schedule (Friday)

8:30 - 9:00 Nicole Rust (NYU) In praise of artifice 9:10 - 9:40 Gidon Felsen (CSHL) Complex cell response sensitivity depends on image statistics

9:50 - 10:10 Break

10:10 - 10:40 Vincent Bonin (Smith-Kettlewell) Dynamic gain control in the responses of lateral geniculate neurons to complex stimuli

10:50 - 11:20 Jack Gallant (UC Berkeley) Using system identification and natural images to reveal new principles of neuronal coding

4:30 - 5:00 Eero Simoncelli (HHMI and NYU) Using models of natural images to understand visual function 5:10 - 5:40 Pamela Reinagel (UCSD) Thalamic bursting in response to natural stimuli 5:50 - 6:10 Break 6:10 - 6:40 Bill Geisler (UT Austin) Bayesian natural scene statistics

6:50 - 7:30 Vijay Balasubramanian (University of Pennsylvania) Natural scene statistics and the organization of the retina

Genetic approaches for systems neuroscience Gero Miesenboeck1 and Susana Lima2

1Yale, 2CSHL

Abstract Ever since the first extracellular recordings by Adrian in the 1920s, the electrode has remained unchallenged as the ruling experimental paradigm in neurophysiology. During the past few years, however, direct molecular manipulations of the mechanisms underlying neuronal excitability, communication, and development have given rise to promising new strategies for observing and controlling the flow of information in neural circuits. The potential and challenges of these approaches, as well as selected examples of biological insights they have yielded, are the focus of this workshop.

Schedule (Friday)

8:30 - 8:40 Introductory remarks 8:40 - 9:00 Aravinthan Samuel (Harvard) Thermotaxis in C. elegans, or how worms take the heat

9:10 - 9:30 Matt Wachowiak (Boston University)

Imaging odor coding and synaptic plasticity in the mammalian brain with genetically-encoded probes

9:40 -10:00 Break 10:00 - 10:20 Florian Engert (Harvard) Visual processing in the developing zebra fish

10:30 - 10:50 Baron Chanda (UCLA) Optical recordings of electrical activity in neurons using hybrid voltage probes

11.00 - 11.20 Sheila Nirenberg (Cornell) Dissecting neural networks using targeted cell class ablation

4:30 - 4:50 Mario de Bono (Medical Research Council) Oxygen sensing and the evolution of foraging behavior in C. elegans

5:00 - 5:20 Herwig Baier (UCSF) Visual perception: From gene to synapse to circuit to behavior in zebrafish

5:30 - 5:50 Bruno van Swinderen (The Neurosciences Institute)

Genes modulating selective attention and associated Local Field Potentials in Drosophila

6:00 - 6:30 Break

6:30 - 6:50 Bruce Baker (Stanford) How are complex innate behaviors built into the nervous system? Emerging lessons from courtship behavior in Drosophila

7:00 - 7:20 Gero Miesenboeck (Yale) Change of Mind: Optical Control of Neuronal Circuits

Parietal cortex: function and computations Jennifer Groh

Dartmouth College

Abstract This workshop will bring together researchers who examine the role of the parietal cortex in behavior. In particular, we bring together researchers who focus on different aspects of parietal activity as it relates to different forms of behavior. This workshop fits well within the goals of the Cosyne meeting and will attract a wide range of the meeting participants. Importantly, the speakers will cover a broad range of topics. By bringing together different groups whose focus on parietal activity is distinct in a forum that supports positive interaction, we hope to a medium in which new research directions can be forged to understanding parietal function

Topics to be presented include:

• What is the role of the parietal cortex in integrating information from different sensory modalities? • What reference-frame transformations occur in the parietal cortex that are needed to mediate action? How are

different modality signal represented in the parietal cortex? • How are decision variables represented in the parietal cortex and what kind of decision variables are observed

in parietal activity? • How does feedback modulate parietal activity and what function does it serve in representations of salience

and attention?

Schedule (Friday)

8:30 - 8:45 Introduction (Jennifer Groh, Dartmouth)

8:45 - 9:05 Tianming Yang (University of Washington)

Representation of log probability by LIP neurons during a probabilistic classification task

9:15 - 9:45 Break

9:45 - 10:05 Michael Platt (Duke University) Attention and reward expectation independently modulate neuronal activity in area LIP

10:15 - 10:45 Jacqueline Gottlieb (Columbia University) Multimodal motor feedback to the lateral intra areaparietal

4:30 - 4:50 Alexandre Pouget (University of Rochester) Optimal spatial representations in the cortexparietal

5:00 - 5:20 Bijan Pesaran (NYU) Dorsal premotor neurons encode the relative position of the hand and eye during reach planning

5:30 - 6:00 Break

6:00 - 6:20 Jennifer Groh (Dartmouth College) Auditory and visual references in the intraparietal sulcus

cosyne computational and systems neuroscience 2006 · of hippocampal place cells ricardo...

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