Final program book

COSYNE

Computational and Systems Neuroscience

2006

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)

1

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)

Organizational support:

Web site support:

Additional Credits

Christina Laycock and the Univ. Rochester Conference & Events Office

Shulamit Avraham

2

Schedule Overview

Cosyne Main Meeting

Marriott–Downtown, Salt Lake City, Utah

Sunday, March 5

7:30 – 8:45PM

8:45 – 9:00

9:00 – 10:00

Reception (Hot and cold hors d'oeuvres & cash bar)

Welcome and announcements

Keynote: Anthony Movshon (NYU) What MT does

Location

Salon F

Salons D–E

Salons D–E

Monday, March 6

7:00 – 8:00 AM

8:00 Slide

1:30 PM

8:00 – 11:00

Continental Breakfast

`

Session

Poster Session I (#39-142)

Foyer

Salons D–E

Salons D–E

Salons F–J

Tuesday, March 7

7:00 – 8:00 AM

8:00

1:30 PM

8:00 – 11:00


3

Session

Poster Session II (#143-245)

Salons D–E

Foyer

Salons D–E

Salons F–J

Wednesday, March 8

7:00 – 8:00 AM

8:00

1:30 PM

5:00 – 5:30


Slide 5

Foyer

Salons D–E

Session Salons D–E

Buses board for The Canyons 100 South Entrance

Cosyne Workshops

The Canyons, Park City, Utah

Thursday, March 9 Location

7:30 – 8:30 AM

8:30 Workshops Parlor rooms

4:30 PM continue Parlor rooms

Friday, March 10

7:30 – 8:30 AM

8:30 Workshops Parlor rooms

4:30 PM

8:00 – 11:30 Banquet continue Parlor rooms

Kokopelli Grand Ballroom

3

Cosyne Main Meeting

Marriott–Downtown

Salt Lake City, Utah

Sunday, March 5

7:30 – 8:45 PM Reception

Hot and cold hors d'oeuvres & cash bar

Plenary Session

8:45 – 10:00 PM

8:45 Welcome and announcements

9:00 Keynote: Anthony Movshon (NYU) What MT does

Salon F

Salons D–E

4

Monday, March 6

7:00 – 8:00 AM Continental breakfast Foyer

Slide Session 1

8:00 – 11:45 AM

Cost of a spike

Salons D–E

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

Making spaces

Foyer

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

Foyer 3:10 – 3:40 Beverage break

3:40 Surya Ganguli (UCSF) Learning and memory in an exactly solvable stochastic spiking network

5

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


#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

Posters #39-142

Salons F–J

6

Tuesday, March 7


Slide Session 3

8:00 – 11:45 AM Salons D–E

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

Slide Session 4

1:30 – 5:15 PM

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

Learning to sing

Salons D–E

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

7

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


#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

Posters #143–245

Salons F–J

8

Wednesday, March 8


Slide Session 5

8:00 – 11:45 AM Salons D–E

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

Views on optimality

Foyer

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

Slide Session 6

1:30 – 4:10 PM Salons D–E

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

9

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

5:30 Last bus departs

Marriott , 100 South Entrance

10

Poster presentations, Session I

Cellular/synaptic

39. Prediction: Linear and Nonlinear Synaptic Integration zones in Basal Dendrites of Neocortical Pyramidal Cells

Bardia F Behabadi

1

Mel

1

1

Haifa, Israel

,

Alon Polsky

2

University of Southern California

,

Jackie Schiller

2 ,

Bartlett W

2

Technion Medical School,

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 Mortimer

1 ,

Peter Dayan

2 ,

Kevin Burrage

1 ,

Geoffrey J

Goodhill

1

1

University of Queensland

Neuroscience Unit

2

Gatsby Computational

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 Anderson

1

1

,

Gregory C DeAngelis

1 ,

J A Movshon

2

Washington Univ. School of Medicine

2

New York University

46. Context dependence of neural responses in rat primary auditory cortex

Hiroki Asari

1 ,

Hysell Oviedo

2 ,

Anthony M Zador

2

1

Watson School of Biological Sciences / Cold Spring Harbor

Laboratory

2

Cold Spring Harbor Laboratory

47. Bayesian inference with probabilistic population codes:

Theory

Jeffrey M Beck

1

1

University of Rochester

Unit

,

Weiji Ma

1 ,

Alexandre Pouget

1 ,

Peter Latham

2

2

Gatsby Computational Neuroscience

48. Adaptation and the role of temporal precision in the visual code

Daniel A Butts

1 ,

Chong Weng

2 ,

Jianzhong Jin

2 ,

Chun-I Yeh

2 ,

11

Nick A Lesica

1

1

,

Jose-Manuel Alonso

2 ,

Garrett B Stanley

1

Harvard University

2

SUNY - 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 Dong

1 ,

Theodore G Weyand

2 ,

Martin Usrey

3

1

Center for Complex Systems and Brain Sciences, Florida Atlantic

University

2

Department 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. Fischer

1 ,

Charles H. Anderson

2

1

California Institute of Technology

2

Washington University

School of Medicine

57. Selectivity, sparseness and information transmission in the inferior temporal visual cortex

Leonardo Franco

1 ,

Edmund T Rolls

2 ,

Jose M Jerez

1 ,

Nick

Aggelopoulos

1

2

University of Malaga

2

University 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


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 Hong

1 ,

Blaise Aguera y Arcas

2 ,

Adrienne L Fairhall

1

1

Department of Physiology and Biophysics, University of

Washington

2

Program 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


66. Is the Cortex a Digital Computer?

Dana H Ballard

,

Janneke FM Jehee


67. Representation of time and states in prefrontal cortex and striatum

Dezhe Z Jin

1 ,

Naotaka Fujii

2 ,

Ann M Graybiel

3

1

Department of Physics, The Pennsylvania State

University

2

Brain Science Institute, RIKEN,

Japan

3

Department 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 Koch

1 ,

Ronen Segev

2 ,

Judith McLean

1 ,

Vijay

Balasubramanian

1

Sterling

1

,

Michael Freed

1 ,

Michael J Berry

2 ,

Peter

1


2


12

70. Selectivity of local field potentials and spikes to the visual stimuli in the human medial temporal lobe

Alexander Kraskov

1 ,

Rodrigo Quian Quiroga

2 ,

Itzhak Fried

3 ,

Christof Koch

1

1

Division of Biology, Caltech

2

Department of Engineering,

University of Leicester, UK

3

Div. 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 Kumar

1 ,

Stefan Rotter

2 ,

Ad Aertsen

3

1

Neurobiology and Biophysics, Insti. of Biology III, Albert-Ludwigs

University Freiburg, Germany

2

Theory and Data Analysis, IGPP,

Freiburg and Bernstein Center for Computational Neuroscience

Freiburg, Germany

3

Neurobiology 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. Latham

1 ,

Arnd Roth

2 ,

Michael Hausser

2 ,

Mickey

London

1

2

Gatsby Computational Neuroscience Unit, UCL

2

Wolfson

Institute for Biomedical Research and Department of Physiology,

UCL

74. Neural Diversity and Ensemble Encoding

Aurel A. Lazar


75. Bayesian inference with probabilistic population codes:

Simulations in a network of conductance-based integrateand-fire neurons

Wei Ji Ma

1 ,

Jeffrey M Beck

1 ,

Peter E Latham

2 ,

Alexandre

Pouget

1

1


2

University College London

76. The representation of interaural time differences in human cortex

David McAlpine

,

Adenike O Deane-Pratt


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 Miura

1

1

,

Masato Okada

2 ,

Shun-ichi Amari

3

Kyoto University / JST PRESTO

2

University of Tokyo / JST

PRESTO / RIKEN BSI

3

RIKEN BSI

Decisions/cognition

79. Formation of attractor representations of abstract rules in cortical networks

Emanuele Curti

1

1

,

Xiao-Jing Wang

2 ,

Stefano Fusi

3

Columbia University, New York, NY

2

Brandeis University,

Waltham, MA

3

Columbia Univ. New York, NY and ETH, Zurich,

Switzerland

80. "Stochastic Multi-stability in Neural Decision-Making

Systems"

Gustavo Deco

1 ,

Alexander Roxin

2 ,

Ralph Andrzejak

2 ,

Daniel

Martí

2

1

ICREA/Universitat Pompeu Fabra

2

Universitat 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 Hwang

1

1

,

Daeyeol Lee

2

Brain & Cognitive Sciences, University of Rochester

Visual Science, University of Rochester

2

Center for

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 Liu

1 ,

Philip J Holmes

2

1

Department of Physics, Princeton University

2

Department 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 Machens

1

1

,

Ranulfo Romo

2 ,

Carlos D Brody

1


2

UNAM Mexico

89. Performance following predicted reward postponement is well explained by temporal discounting.

Takafumi Minamimoto

,

Giancarlo LaCamera

,

Barry J Richmond

NIMH, NIH

Learning/plasticity

13

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


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/calmodulindependent 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 Moldakarimov

1 ,

James L McClelland

2 ,

Bard G

Ermentrout

1

1

University of Pittsburgh

2

Carnegie Mellon Unibersity

Motor/sensorimotor

102. Adaptive control and the flow of information in the brain

Mohamed N Abdelghani

1 ,

Timothy P Lillicrap

2 ,

Douglas B

Tweed

1

3

University of Toronto

Toronto

2

Queens University

3

Univeristy of

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


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 Ghose

1

3

,

Timothy K Horiuchi

2 ,

P. S. Krishnaprasad

2 ,

Cynthia F Moss

1

Dept. Psychology, Neuroscience and Cognitive Science Program,

University of Maryland, College Park

2

Dept. Electrical and

Computer Engineering, Neuroscience and Cognitive Science

Program, Institute for Systems Research, University of Maryland,

College Park

3

Dept. 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 Olasagasti

1 ,

Emre Aksay

2 ,

Guy Major

2

Mark S Goldman

1

1

Wellesley College

2


,

David W Tank

2 ,

108. A unified optimal control treatment of reaching and tracking

Dongsung Huh

,

Emanuel Todorov

UCSD

109. Neuromechanical Modeling of Zebrafish Locomotion

Etienne Hugues

1 ,

Donald P Knudsen

2 ,

John A Arsenault

2 ,

Donald M O'Malley

2

1

SUNY at Buffalo

,

Jorge V Jose

1

2

Northeastern 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 Assisi

1 ,

Mark Stopfer

2 ,

Gilles Laurent

3 ,

Maxim

Bazhenov

1

1

The Salk Institute for Biological Studies

2

NIH-

NICHD

3


112. Biologically realistic neural inhibition in arbitrary neural circuits

Christopher Parisien

1 ,

Charles H. Anderson

2

1

University of Waterloo

,

Chris Eliasmith

2

Washington University in St. Louis

1

113. On the Dynamics of Electrically-coupled Neurons with

Inhibitory Synapses

Juan Gao

1 ,

Philp Holmes

2

Engineering

1

Department of Mechanical and Aerospace

2

Department 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 Ishibashi

1 ,

Kosuke Hamaguchi

2

1

Univ. of Tokyo / JST PRESTO

JST PRESTO / RIKEN BSI

,

Masato Okada

3

2

RIKEN BSI

3

Univ. of Tokyo /

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 Leibold

1 ,

Kay Thurley

1 ,

Anja Gundlfinger

2 ,

Robert

Schmidt

1 ,

Dietmar Schmitz

2 ,

Richard Kempter

1

Institute for Theoretical Biology HU Berlin

Berlin

1

2

NSRC Charite

119. Mean Field Theory with Cross-Correlations for a Cortical

Network Model

Alexander Lerchner

1

1

,

John Hertz

2

Laboratory of Neuropsychology, NIMH/NIH/DHHS

2

NORDITA

120. Robust Propagation of Bursts in Noisy Heterogeneous

Synfire Chains

Meng-Ru Li

,

Henry Greenside

Duke University

14

Sensory/perception/attention

121. Do within modality and cross-modality sensory integration follow the same rules?

Ulrik R Beierholm

1

1

Caltech

2

,

Steven R Quartz

1 ,

Ladan Shams

2

UCLA - Dept. of Psych.

122. Spike timing in mechanoreceptive afferent fibers can be predicted using integrate-and-fire mechanisms.

Sliman J Bensmaia

1

1

,

Arun P Sripati

2


2

Center for the Neural Basis of

Cognition

123. Brain-Inspired Neural Model of Visual Attention for

Multiple Object Tracking

Roman Borisyuk

1 ,

Yakov Kazanovich

2

1

University of Plymouth, United Kingdom

2

Institute 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


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 Clifford

1 ,

Szonya Durant

1 ,

Nathan A Crowder

2 ,

Nicholas SC Price

2

1

,

Michael R Ibbotson

2

University of Sydney

2

Australian National University

128. Multivariate Analysis of Frontal Eye Field Activity during

Visual Search

Jeremiah Y Cohen

1 ,

Pierre Pouget

2 ,

Chenchal Rao

2 ,

Jeffrey D

Schall

2 ,

Andrew F Rossi

2

1

Vanderbilt Brain Institute

Psychology

2

Vanderbilt University Department of

129. Spectral receptive field properties explain shape selectivity in V4

Stephen V David

1

1

,

Benjamin Y Hayden

2 ,

Jack L Gallant

2

University of Maryland

2

University 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 Gold

1 ,

Cyrille Girardin

2 ,

Rodney Douglas

2 ,

Christof Koch

1

1

Computation and Neural Systems, California Institute of

Technology

2

Institute of Neuroinformatics, Swiss Federal

15

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 Hugues

1

1

,

Scott A Hill

2 ,

Paul H Tiesinga

3 ,

Jorge V José

1

SUNY at Buffalo

2

CIRCS, Northeastern University

3

University of North Carolina at Chapel Hill

135. Functional topology of attention in the pulvinar

Oliver Hulme

1 ,

Justin Chumbley

1 ,

Simon B Eickhoff

2 ,

Simon

1

Prince

1 ,

Stuart Shipp

1


2

Research Center Julich

136. Feedback-mediated facilitation from the "far" receptive field surround of macaque V1 neurons

Jennifer M Ichida

1

1

,

Lars Schwabe

2 ,

Paul C Bressloff

3 ,

Alessandra Angelucci

1

Moran Eye Center, University of Utah, Salt Lake City, UT,

USA

2

Electrical Enginering & Computer Science, TU Berlin,

Germany

3

Department 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 Liu

1

1

,

Christoph E Schreiner

2

Emory University

2

UCSF

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 Omori

1 ,

Toru Aonishi

2 ,

Hiroyoshi Miyakawa

3 ,

Masashi

Inoue

3 ,

Masato Okada

4

1

PRESTO, Japan Science and Technology Agency /

RIKEN

2

Tokyo Institute of Technology / RIKEN

3

Tokyo

University of Pharmacy and Life Science

4

The 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 Prescott

1

1

,

Stéphanie Ratté

2 ,

Yves De Koninck

3 ,

Terrence J Sejnowski

4

Computational Neurobiology Laboratory, The Salk Institute, La

Jolla, CA 92037

2

Département de physiologie, Université de

Montréal, Montréal, Québec, Canada H3C 3J7

3

Division 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

4

Computational

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 Tobin

1 ,

Ronald L Calabrese

1

Brandeis University

2

2

Emory University

Coding/computation

149. Entorhinal Input and the Remapping of Hippocampal

Place Fields

Joseph D Monaco

1 ,

Isabel A Muzzio

2 ,

Liat Levita

2 ,

L F Abbott

1

1

Center for Theoretical Neuroscience, Columbia

University

2

Center for Neurobiology & Behavior, Columbia

University

150. Spatial and temporal organization of glomerular

16 representation in the moth antennal lobe

Shigehiro Namiki

1

1

,

Ryohei Kanzaki

2 the University of Tsukuba

2 the University of Tokyo

151. Efficient Neural Burst Analysis

Rama Natarajan

1

1

,

Farzan Nadim

2


2

Rutgers 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 spiketriggered average and covariance analysis

Jonathan W Pillow

1

1

,

Eero P Simoncelli

2


2

HHMI and


154. Visual acuity in the presence of fixational eye movements

Xaq Pitkow

1 ,

Haim Sompolinsky

1


2 ,

Markus Meister

2

Hebrew University

1

155. Natural scene statistics predict that larger ganglion cells should have relatively smaller surrounds

Charles P Ratliff

,

Peter Sterling

,



156. Solving the stereo correspondence problem with hybrid position and phase disparity detectors

Jenny Read

1

1

,

Bruce Cumming

2

Newcastle University

2

National Eye Institute

157. Extracting low-dimensional task representations from neural signals

James Rebesco

1 ,

Sara A Solla

2 ,

Lee E Miller

1

1

Department of Physiology, Northwestern

University

2

Department 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


162. Probing the structure of multi-neuron firing patterns in the primate retina using maximum entropy methods

Jonathon Shlens

1 ,

Greg D Field

1 ,

Jeff L Gauthier

1 ,

Matthew I

Grivich

2 ,

Dumitru Petrusca

2

Chichilnisky

1

1

Salk Institute

2

,

Alexander Sher

2 ,

Alan M Litke

2 ,

EJ

UC Santa Cruz

163. Hybrid Discrete/Continuous Models of Brain Dynamics:

Estimation from Spikes

Lakshminarayan Srinivasan

1

Emery N Brown

1

4

,

Uri T Eden

2 ,

Sanjoy K Mitter

3 ,

MIT Department of Electrical Engineering & Computer Science/

Harvard Medical School, Health Sciences & Technology Track/

Massachusetts General Hospital

2

HMS-MIT HST/MGH

EECS Laboratory of Information & Decision Systems

4

3

MIT

MIT

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


165. Distinct roles of synaptic connectivity and refractoriness on the spike-based and rate-based population decoding

Taro Toyoizumi

1 ,

Kazuyuki Aihara

2 ,

Shun-ichi Amari

3

1

Department of Complexity Science and Engineering, University of Tokyo

2

Institute of Industrial Science, University of

Tokyo

3


166. Sniffing cycle-based odor coding in the anterior olfactory cortex

Naoshige Uchida

,

Zachary F Mainen


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


170. Behaviorally-dependent information processing in a songbird circuit required for vocal plasticity

Brian D Wright

1 ,

Mimi H Kao

2 ,

Allison J Doupe

1

UCSF, Sloan-Swartz Center for Theoretical

2

3

Neurobiology UCSF, Dept. of Physiology

Physiology and Psychiatry

3

UCSF, Depts. of

Decisions/cognition

171. A Simulation of the Uniform Selection Hypothesis in a

Delayed-response Task

17

Ahmed A. Moustafa

1 ,

Anthony S. Maida

2

1

Institute of Cognitive Science, University of Louisiana at

Lafayette, Lafayette, LA 70504

2

Center 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


174. State-dependent matching law in stochastic gradient ascent

Yutaka Sakai

1 ,

Tomoki Fukai

2

1

Human Informatics Course, Faculty of Engineering, Tamagawa

University, Japan

2

Brain Science Institute, RIKEN, Japan

175. Different subregions of human striatum encode appetitive and aversive outcomes in mixed prospect predictive learning of money.

Ben Seymour

1 ,

Nathaniel Daw

2 ,

Peter Dayan

2 ,

Tania Singer

3 ,

Ray Dolan

1

1

Wellcome Department of Imaging Neuroscience, UCL

2

Gatsby

Compuational Neuroscience Unit, UCL

3

Institute 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 Wong

1

Jing Wang

1

,

Alexander C Huk

1


2 ,

Michael N Shadlen

3 ,

Xiao-

2

University of Texas at Austin

3

University of Washington, Seattle

178. Bidirectional spike-timing dependent plasticity of inhibitory transmission in the hippocampus

Jake Ormond

,

Melanie A Woodin


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


1 ,

James L McClelland

2 ,

Bard G

Ermentrout

1

1


2

Carnegie 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


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 Turaga

1

1

,

Haim Sompolinsky

2 ,

H. Sebastian Seung

1

MIT

2

The Hebrew University

187. A Hebbian reinforcement learning algorithm reproducing monkey performances in visuo-motor learning task.

Eleni Vasilaki

1 ,

Stefano Fusi

2 ,

Xiao-Jing Wang

3 ,

Walter Senn

1

Institute of Physiology, University of Bern, Switzerland for Neuroinformatics (INI), ETH, Zurich, Switzerland

3

Volen

1

2

Institute

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


192. Retinal lesion-induced receptive field reorganization in primary visual cortex is spike timing dependent

18

Joshua M Young

1 ,

Bogdan Dreher

2 ,

Klaus Obermayer

1

1

Neural Information Processing Group, Department of Computer

Science, Berlin University of Technology, Germany / Bernstein

Center for Computational Neuroscience Berlin,

Germany

2

Institute for Biomedical Research, The University of

Sydney, Australia

193. Learning by message-passing in networks of discrete synapses

Riccardo Zecchina

1

1

,

Alfredo Braunstein

2

International Centre for Theoretical Physics (ICTP)

Scientific Interchange (ISI)

2

Institute for

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 Rubin

1 ,

Yixin Guo

2 ,

Cameron McIntyre

3 ,

Kresimir

Josic

4

1

,

David Terman

5


2

Drexel University

3

Cleveland

Clinic

4

University of Houston

5

The Ohio State University

196. A biophysical approach to behavioral neuroscience in C. elegans

Aravinthan Samuel


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


199. A local tuning model predicts adaptation rate and degree of generalization of visuomotor rotation learning

Hirokazu Tanaka

1

1

,


1 ,

John W Krakauer

2

Computational Neurobiology Laboratory, Salk Institute

Performance Laboratory, Columbia University

2

Motor

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

,



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 Nesse

1

1

,

Christopher R Butson

2 ,

Gregory A Clark

2

University of Utah Department of Mathematics

2

University 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 Saraga

1 ,

Leo Ng

2 ,

Frances K Skinner

3

1

Department of Zoology, Department of Physiology, Toronto

Western Research Institute, University Health Network, University of Toronto

2

Engineering Science Program, University of

Toronto

3

Toronto Western Research Institute, University Health

Network, Department of Medicine (Neurology), Department of

Physiology, Institute of Biomaterials and Biomedical Engineering,


211. Periodic Bursting in Two Identical Coupled Cell Systems

LieJune Shiau

1

1

,

Marty Golubitsky

2 ,

Kresimir Josic

2

University of Houston-Clear Lake

2

University 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

19


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 Webber

1

Technology


,

Garrett B Stanley

2

1

Harvard - MIT Division of Health Sciences and

2

Division of Engineering and Applied Sciences,

217. Synaptic Mechanisms of Thalamocortical Auditory

Processing

Mike Wehr

University of Oregon

218. Recurrent Network Models for Working Memory of

Temporal Sequences

Olivia L White

1

1

,

Avigail Ben Or

2 ,

Haim Sompolinsky

2

MIT, Department of Physics

2

Hebrew 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 Orban

1

1

,

Jozsef Fiser

2 ,

Richard N Aslin

3 ,

Mate Lengyel

4

Collegium Budapest

2

Volen Center for Complex Systems,


3

Department of Brain and Cognitive

Sciences, University of Rochester

4

Gatsby Computational

Neuroscience Unit, University College London

225. State-dependence differences in evoked responses in auditory cortex

Gonzalo H Otazu

,

Anthony M Zador


226. Illusory Percepts from Auditory Adaptation

Lucas C. Parra

1 ,

Barak A. Pearlmutter

2

1

City College New York

2

Hamilton 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


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 Sharma

1 ,

Beau Cronin

1 ,

Klaus Wimmer

2 ,

Konrad

Körding

1

1

,

James Schummers

1 ,

Klaus Obermayer

2 ,

Mriganka

Sur

1

Dept. of Brain and Cognitive Sciences, Picower Center for

Learning and Memory, MIT, Cambridge, MA, USA

2

Dept. 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 Snodderly

1 ,

Igor Kagan

2

1

Medical College of Georgia

,

Moshe Gur

2

Caltech

3

3

Technion, Israel

235. Heterogenous firing rate dependencies in simultaneously recorded neural populations in cat area 17

Martin A Spacek

1

1

,

Timothy J Blanche

2 ,

Nicholas V Swindale

1

University of British Columbia

2

Hanse-Wissenschaftskolleg,

Brain Research Institute, University of Bremen

20

236. Adaptation within a Bayesian Framework for Perception

Alan A Stocker

,

Eero P Simoncelli

Howard Hughes Medical Institute and Center for Neural Science,


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 Szuts

1 ,

Edward Soucy

2 ,

Alan Litke

3 ,

Athanassios Siapas

4 ,

Markus Meister

1

2

Program in Biophysics, Harvard University

2

Department of

Molecular and Cellular Biology, Harvard University

3

Santa Cruz

Institute for Particle Physics, University of California Santa

Cruz

4

Division 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


240. Multiple S-cone pathways in the macaque visual system

Chris Tailby

1

1

,

Samuel G Solomon

2 ,

Peter Lennie

1


2

University 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


244. A Bayesian View of Sensory Conflicts in Decision-

Making

Angela J Yu

1

1

,

Peter Dayan

2 ,

Jonathan D Cohen

1


2

Gatsby 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

L F Abbott


Ad Aertsen

Nick Aggelopoulos

Blaise Aguera y Arcas

Kazuyuki Aihara

Emre Aksay

Mark V. Albert

Jose-Manuel Alonso

Shun-ichi Amari

Maxime Ambard

Ramin Amirnovin

Richard Andersen

Charles H. Anderson

Ralph Andrzejak

Dora Angelaki

Alessandra Angelucci

Toru Aonishi

Majid Arabgol

John A Arsenault

Hiroki Asari

Ana A Ashurova

Richard N Aslin

Collins G Assisi

David Attwell

Bruno B Averbeck

Baktash Babadi

Wyeth Bair


Dana H Ballard

Peter Bandettini

Maxim Bazhenov

Jeffrey M Beck

Suzanna Becker

Bardia F Behabadi

Ulrik R Beierholm

Marina A Belova

Avigail Ben Or

Sliman J Bensmaia

Hagai Bergman

Michael J Berry

Guo-Qiang Bi

William Bialek

Hugh T Blair

Timothy J Blanche

Roman Borisyuk

Kristofer E Bouchard

David C Bradley

Michael S Brainad

David H. Brainard

Alfredo Braunstein

Paul C Bressloff

Ken H Britten

Carlos D Brody

Emery N Brown

Nicolas Brunel

Calin I Buia

Neil Burgess

Kevin Burrage

Christopher R Butson

Daniel A Butts

Patrick A Byrne

Charles Cadieu

Xinying Cai

Ronald L Calabrese

Ran Carmi

Ryan S Carraway

Frances S Chance

Steven M Chase

Frédéric Y Chavane

Ricardo Chavarriaga

YeTing H Chen

Yuzhi Chen

Author Index (main meeting only)

lfa2103@columbia.edu mohamed.abdelghani@utoronto.ca aertsen@biologie.uni-freiburg.de na@psy.ox.ac.uk blaise@sandcodex.com aihara@sat.t.u-tokyo.ac.jp eaksay@Princeton.EDU mva6@cornell.edu jalonso@mail.sunyopt.edu amari@brain.riken.go.jp mambard@yahoo.fr ramirnovn@partners.org andersen@vis.caltech.edu cha@wustl.edu ralphandrzejak@yahoo.de alessandra.angelucci@hsc.utah.edu aonishi@dis.titech.ac.jp arabgol@ipm.ir arsenault.j@neu.edu asari@cshl.edu ma20008399@aol.com aslin@bcs.rochester.edu collins@salk.edu baverbeck@cvs.rochester.edu baktash@ipm.ir wyeth@physiol.ox.ac.uk vijay@physics.upenn.edu dana@cs.rochester.edu bandettini@nih.gov

149

102, 202

72

57

63

165

107

43

48

78, 165

44

55

30, 200

45, 56, 112

80

111

3

28

90

62

58, 69, 155

66,173

138

19

136

143

90

109

46

167

224 bazhenov@salk.edu jbeck@bcs.rochester.edu becker@mcmaster.ca behabadi@usc.edu beierh@caltech.edu mab2058@columbia.edu avigail@pob.huji.ac.il sliman@jhu.edu hagaib@md.huji.ac.il berry@Princeton.EDU gqbi@pitt.edu wbialek@princeton.edu blair@psych.ucla.edu cosyne@timblanche.mm.st rborisyuk@plymouth.ac.uk kris@phy.ucsf.edu bradley@uchicago.edu msb@phy.ucsf.edu brainard@psych.upenn.edu braunstein@isi.it bressloff@math.utah.edu khbritten@ucdavis.edu brody@cshl.edu brown@neurostat.mgh.harvard.edu nicolas.brunel@univ-paris5.fr buia@physics.unc.edu kb@maths.uq.edu.au cbutson@utah.edu dbutts@deas.harvard.edu byrne@psychology.mcmaster.ca cadieu@berkeley.edu

111

47,75

93

39

121

91

218

122

207

69

92

31,198

6

235

123

124

242

124

58

193

136, 183, 205

141, 245

88

55, 163

95

241

5

41

204

48

93

116 xinying.cai@asu.edu rcalabre@biology.emory.edu carmi@usc.edu rsc041000@utdallas.edu

94

148

125

182 fchance@uci.edu schase@bme.jhu.edu

68

49

Frederic.Chavane@incm.cnrs-mrs.fr 152 ricardo.chavarriaga@a3.epfl.ch 50 ytc013000@utdallas.edu chen@mail.cps.utexas.edu

182

126

21

Szonya Durant

Alexander Ecker

Uri T Eden

Simon B Eickhoff

Chris Eliasmith

Bard G Ermentrout

Emad N Eskandar

Adrienne L Fairhall

Michael Fee

Claudia E Feierstein

David J. Field

Greg D Field

Michael S. Fine

Leif H Finkel

Brian J. Fischer

Jozsef Fiser

Paul J Fitzgerald

Kristen P Fortney

Leonardo Franco

Eric S Frechette

Michael Freed

Itzhak Fried

Robert C Froemke

Naotaka Fujii

Tomoki Fukai

Stefano Fusi

Christopher V Gabel

Jack L Gallant

Surya Ganguli

Juan Gao

Jose A Garcia-Lazaro

Daniel Gardner

EJ Chichilnisky

Justin Chumbley

Gregory A Clark

Colin WG Clifford

Jeremiah Y Cohen

Jonathan D Cohen

Mary M Conte

Beau Cronin

Beau D Cronin

Patrick Crotty

Nathan A Crowder

Bruce G Cumming

Emanuele Curti

Mohammad Dastjerdi

Stephen David

Stephen V David

Nathaniel Daw

Peter Dayan

Yves De Koninck

Gregory C DeAngelis

Adenike O Deane-Pratt

Gustavo Deco

Chris Deister

Bertrand Delgutte

Kate S Denning

Gaelle Desbordes

Sasha Devore

Eizaburo Doi

Ray Dolan

Dawei W Dong

John P Donoghue

Rodney Douglas

Allison J Doupe

Bogdan Dreher

Patrick Garrigan

Jeff L Gauthier

Wilson S Geisler

Wulfram Gerstner

Kaushik Ghose

Cyrille Girardin

Paul W. Glimcher

Carl Gold

David H Goldberg

Joshua A Goldberg

Mark S Goldman

Tim Gollisch ej@salk.edu jrchumbley@yahoo.com

Greg.Clark@utah.edu colinc@psych.usyd.edu.au jeremiah.y.cohen@vanderbilt.edu jdc@princeton.edu mmconte@med.cornell.edu bcronin@MIT.EDU bcronin@mit.edu prc9m@virginia.edu nathan.crowder@anu.edu.au bgc@lsr.nei.nih.gov ec2334@columbia.edu dastjerdi@ccs.fau.edu svd@umd.edu svd@umd.edu daw@gatsby.ucl.ac.uk dayan@gatsby.ucl.ac.uk yves.dekoninck@crulrg.ulaval.ca gregd@cabernet.wustl.edu adenike.turner@ucl.ac.uk gustavo.deco@upf.edu chris.deister@utsa.edu

Bertrand_Delgutte@meei.harvard.edu kdenning@biomail.ucsd.edu gdesbord@cns.bu.edu sashad@mit.edu edoi@cnbc.cmu.edu r.dolan@fil.ion.ucl.ac.uk dawei@dove.ccs.fau.edu

John_Donoghue@Brown.edu rjd@ini.phys.ethz.ch ajd@phy.ucsf.edu bogdand@anatomy.usyd.edu.au

25, 162

135

204

127

128

244

167

231

51

4

127

132, 156, 221

79

52

77

129

175

7, 21, 36, 41, 82, 175, 244

130

53

175

52, 54

201

131

24, 170, 220, 232

192

145

45

76

80

40

130

158

15 szonyad@psych.usyd.edu.au alexander.ecker@tuebingen.mpg.de tzvi@neurostat.mgh.harvard.edu

S.Eickhoff@fz-juelich.de celiasmith@uwaterloo.ca bard@math.pitt.edu eeskandar@partners.org fairhall@u.washington.edu

127

26

55, 163

135

112, 215

181

55

63 feierste@cshl.edu djf3@cornell.edu gfield@salk.edu

22

81

43

162 msf1@cec.wustl.edu 103 leif@neuroengineering.upenn.edu 169 brian@etho.caltech.edu fiser@brandeis.edu

56

224 pfitz@mbi.mb.jhu.edu virian@yahoo.com lfranco@lcc.uma.es frechette@ucsd.edu

164

104

57

25 michael@retina.anatomy.upenn.edu 69 ifried@mednet.ucla.edu 70 rfroemke@phy.ucsf.edu na@fujiis.com

12

67 tfukai@brain.riken.jp fusi@ini.unizh.ch gabel@fas.harvard.edu gallant@socrates.berkeley.edu surya@faure.ucsf.edu jgao@princeton.edu jagl@physiol.ox.ac.uk dgardner@med.cornell.edu

174,213

79,187

105

129

11

113

230

59 pg@sas.upenn.edu gauthier@salk.edu geisler@psy.utexas.edu wulfram.gerstner@epfl.ch kaushik.ghose@gmail.com cyrilleg@ini.phys.ethz.ch glimcher@cns.nyu.edu carlg@caltech.edu dhg2002@med.cornell.edu joshua.goldberg@utsa.edu markg@princeton.edu gollisch@fas.harvard.edu

58

162

126

50, 185

106

131

37

131

59

40

107

60

22

Dongsung Huh

Alexander C Huk

Oliver Hulme

Quentin JM Huys

Jaewon Hwang

Michael R Ibbotson

Jennifer M Ichida

Masashi Inoue

Ramon Iovin

Kazuya Ishibashi

Shin Ishii

Toshiyuki Ishii

Laurent Itti

Vikram Jakkamsetti

Santiago Jaramillo

Vivek Jayaraman

Janneke FM Jehee

Jose M Jerez

Dezhe Z Jin

Jianzhong Jin

Jorge V Jose

Kresimir Josic

Jorge V José

Joseph K Jun

Oliver Kacelnik

Igor Kagan

Ryohei Kanzaki

Mimi H Kao

William L Kath

Yael Katz

Yakov Kazanovich

Georgios A Keliris

Marty Golubitsky

Geoffrey J Goodhill

Arnulf B.A. Graf

Michael Graupner

Ann M Graybiel

Henry Greenside

Matthew I Grivich

Robert Guetig

Anja Gundlfinger

Yixin Guo

Moshe Gur

Ralf M Haefner

Stefan Haeusler

Kosuke Hamaguchi

Michael E Hasselmo

Michael Hausser

Benjamin Y Hayden

Jiping He

David J Heeger

Nicolas Heess

Gali Heimer

John Hertz

Andreas VM Herz

Scott A Hill

Shizuko Hiryu

Simon SM Ho

Philip J Holmes

Lori L Holt

Sungho Hong

Timothy K Horiuchi

Toshihiko Hosoya

Tomas Hromadka

Steven S Hsiao

Etienne Hugues

Richard Kempter

Adam Kepecs

Peyman Khorsand

Michael P Kilgard

Sung S Kim

Eyal Y Kimchi

Andrew J King

Jennifer M. Klein

Ulf Knoblich

Donald P Knudsen

Christof Koch

Kristin Koch mg@math.uh.edu g.goodhill@uq.edu.au

211

41 arnulf.graf@nyu.edu 61 michael.graupner@univ-paris5.fr 95 graybiel@MIT.EDU hsg@phy.duke.edu

67

120 mgrivich@scipp.ucsc.edu guetig@cc.huji.ac.il anja.gundlfinger@charite.de yigst@math.ohio-state.edu

25, 162

96

118

195 mogi@bm.technion.ac.il haefnerr@nei.nih.gov haeusler@igi.tugraz.at hammer@brain.riken.jp hasselmo@bu.edu m.hausser@ucl.ac.uk hayden@neuro.duke.edu hjp@asu.edu

234

132

114

115

99

73

129

94 david.heeger@nyu.edu nmo@physiol.ox.ac.uk galih@md.huji.ac.il hertz@nordita.dk

139, 222

62

207

119 a.herz@biologie.hu-berlin.de shill@tower.par64.org

32

134 etd1101@mail4.doshisha.ac.jp 35 simon.ho@oriel.ox.ac.uk 230 pholmes@Math.Princeton.EDU lholt@andrew.cmu.edu shhong@u.washington.edu timmer@isr.umd.edu hosoya@brain.riken.jp hromadka@cshl.edu steven.hsiao@jhu.edu ehugues@buffalo.edu

86, 113

233

63

106

64

133

164

109,134 dhuh@ucsd.edu huk@mail.utexas.edu o.hulme@ucl.ac.uk qhuys@gatsby.ucl.ac.uk

108

177

135

21, 82 jhwang@bcs.rochester.edu 83 ibbotson@rsbs.anu.edu.au 127 jennifer.ichida@hsc.utah.edu 136 inou@ls.toyaku.ac.jp 143 riovin@bu.edu kazuya@mns.k.u-tokyo.ac.jp ishii@is.naist.jp tishii@brain.riken.jp itti@usc.edu vxj037000@utdallas.edu sjara@ieee.org vivek@caltech.edu

15

115

179

64

17,125

182

137

65 jehee@cs.rochester.edu jja@lcc.uma.es djin@phys.psu.edu jjin@mail.sunyopt.edu jjosev@research.buffalo.edu josic@math.uh.edu

JJosev@research.buffalo.edu juj12@psu.edu

66

57

67, 97

48

109

195, 211

134

97 ok@physiol.ox.ac.uk igor@vis.caltech.edu

98

234 kanzaki@i.u-tokyo.ac.jp 150 mimi@phy.ucsf.edu 170 kath@northwestern.edu y-katz@northwestern.edu

42

42 yakov_k@impb.psn.ru 123 georgios.keliris@tuebingen.mpg.de 26 r.kempter@biologie.hu-berlin.de kepecs@cshl.edu pkhorsan@uci.edu kilgard@utdallas.edu sskim@jhu.edu eyal.kimchi@yale.edu ajk@physiol.ox.ac.uk jmklein@sas.upenn.edu knoblich@csail.mit.edu knudsen.d@neu.edu koch@klab.caltech.edu kochk@mail.med.upenn.edu

118

84, 180

68

182

164

110

98

58

116

109

70, 131

69

23

Alan M Litke

Robert C Liu

Yuan Liu

Zeng Liu

Yonatan Loewenstein

Nikos K Logothetis

Mickey London

Kenway Louie

Jörg Lücke

Wei Ji Ma

Weiji Ma

Wolfgang Maass

Christian K Machens

Anthony S. Maida

Zachary F Mainen

Ania Majewska

Guy Major

Dominique Martinez

Daniel Martí

Michael D Mauk

John Maunsell

David McAlpine

James L McClelland

Christin H McCool

Cameron McIntyre

Judith McLean

Markus Meister

Bartlett W Mel

Michael M Merzenich

Nima Mesgarani

Lee E Miller

Takafumi Minamimoto

Randal A Koene

Adam Kohn

Minjoon Kouh

John W Krakauer

Alexander Kraskov

Gabriel Kreiman

Jens Kremkow

Nikolaus Kriegeskorte

P. S. Krishnaprasad

Jayant E Kulkarni

Arvind Kumar

Konrad Körding

Darin R La Sota

Giancarlo LaCamera

Jonas Larsson

Peter E. Latham

Pak-Ming Lau

Mark Laubach

Gilles Laurent

Aurel A. Lazar

Daeyeol Lee

Robert Legenstein

Christian Leibold

Mate Lengyel

Peter Lennie

Alexander Lerchner

Nick A Lesica

Liat Levita

William B Levy

Michael S Lewicki

Meng-Ru Li

Timothy P Lillicrap

Stephen G Lisberger

Alan Litke

Jude F Mitchell

Sanjoy K Mitter

Keiji Miura

Hiroyoshi Miyakawa


Joseph D Monaco

Christopher I Moore

Duncan Mortimer

Cynthia F Moss

Ahmed A. Moustafa

Anthony Movshon

J A Movshon randalk@bu.edu adamk@cns.nyu.edu

99

61 kouh@mit.edu 116 jkrakauer@neuro.columbia.edu 199 kraskov@klab.caltech.edu 70 kreiman@mit.edu 116

Jen.Kremkow@incm.cnrs-mrs.fr niko@nih.gov krishna@isr.umd.edu jk2619@columbia.edu

152

138

106

71 arvind.kumar@biologie.uni-freiburg.de konrad@koerding.de lasota.d@neu.edu lacamerag@mail.nih.gov

72

18,51,231

212

85, 89 jonas@cns.nyu.edu 139 pel@gatsby.ucl.ac.uk 47, 73, 75 plau@pitt.edu mark.laubach@yale.edu

92

110 laurentg@its.caltech.edu aurel@ee.columbia.edu dlee@cvs.rochester.edu legi@igi.tugraz.at

65,111

74

28,83

117 c.leibold@biologie.hu-berlin.de 118 lmate@gatsby.ucl.ac.uk 7, 224

LerchnerA@mail.nih.gov

2, 240

119 lesica@fas.harvard.edu ll2250@columbia.edu wbl@virginia.edu lewicki@cnbc.cmu.edu mrli@phy.duke.edu tim@biomed.queensu.ca sgl@phy.ucsf.edu alan.litke@cern.ch

48

149

4

53

120

102

14, 197

238

Alan.Litke@cern.ch robert.liu@emory.edu yuanliu@Princeton.EDU bjr@ln.nimh.nih.gov yonatanl@mit.edu nikos.logothetis@tuebingen.mpg.de m.london@ucl.ac.uk klouie@cns.nyu.edu lucke@gatsby.ucl.ac.uk weijima@gmail.com weijima@bcs.rochester.edu maass@igi.tugraz.at machens@cshl.edu maida@cacs.louisiana.edu zach@cshl.edu majewska@mit.edu

25, 162

140

86

85

87

26

73

37

100

75

47

114, 117

88

171

81, 84, 166, 172, 180

189 gmajor@princeton.edu

Dominique.Martinez@loria.fr

107

44 daniel.marti@upf.edu 80

Michael.D.Mauk@uth.tmc.edu 188 d.mcalpine@ucl.ac.uk jlm@cnbc.cmu.edu cdhansen@ucdavis.edu

29

76

181

141 mcintyc@ccf.org judy@retina.anatomy.upenn.edu meister@fas.harvard.edu mel@usc.edu merz@phy.ucsf.edu mnima@umd.edu lm@northwestern.edu minamimotot@mail.nih.gov

195

69

154, 238

39, 191

12

77

157

89 jude@salk.edu mitter_removethisstring@mit.edu miura@ton.scphys.kyoto-u.ac.jp miyakawa@ls.toyaku.ac.jp sam47@pitt.edu joe@neurotheory.columbia.edu cim@mit.edu dmorti@gmail.com cmoss@psyc.umd.edu halimo19@hotmail.com movshon@nyu.edu

142, 237

163

78

143

181

149

227

41

34, 106

171

1

45

24

Eran A Mukamel

Paul W. Munro

Isabel A Muzzio

Farzan Nadim

Katherine Nagel

Shigehiro Namiki

Nandakumar S Narayanan

Rama Natarajan

Vidhya Navalpakkam

Matthew J Nelson

William H Nesse

Leo Ng

Hendrikje Nienborg

Fernando R Nodal

Crystal T Novitski

Duane Q Nykamp

Donald M O'Malley

Klaus Obermayer

Shani Offen

Tatsuya Ohyama

Masato Okada

Itsaso Olasagasti

Cheryl A Olman

Toshiaki Omori

Gergo Orban

Jake Ormond

Andrew M Oster

Gonzalo H Otazu

Hysell Oviedo

Liam Paninski

Christopher Parisien

Lucas C. Parra

Carl H Parsons

Naama Parush

Joseph J Paton

Barak A. Pearlmutter

Yefei Peng

Claudia A Perez

Laurent U Perrinet

Matthew S Perry

Bijan Pesaran

Michael J Pesavento

Dumitru Petrusca

Jean-Pascal Pfister

Jonathan W Pillow

David J Pinto

Xaq Pitkow

Dietmar Plenz

Tomaso Poggio

Alon Polsky

Alexandre Pouget

Pierre Pouget

Steven A Prescott

Nicholas SC Price

Simon Prince

Astrid A Prinz

Dominique L Pritchett

Amanda C Puckett

Gopathy Purushothaman

Steven R Quartz

Rodrigo Quian Quiroga

Ithai Rabinowitch

Marcus Raichle

Sachin P Ranade

Chenchal Rao

Charles P Ratliff

Stéphanie Ratté

Jenny Read

James Rebesco

Pamela Reinagel

Alexandre Reynaud

John H Reynolds

Barry J Richmond

Hiroshi Riquimaroux

Jason Ritt

Carson B Roberts

Robert Rohrkemper

Edmund T Rolls emukamel@stanford.edu pmunro@mail.sis.pitt.edu im128@columbia.edu farzan@stg.rutgers.edu

190, 203

184

149

151 knagel@phy.ucsf.edu 197, 220, 232 namiki@brain.imi.i.u-tokyo.ac.jp 150 kumar.narayana@yale.edu rama@cs.toronto.edu navalpak@usc.edu nelsonmj@vis.caltech.edu

110

21,151

17

30 nesse@math.utah.edu l.ng@utoronto.ca hn@lsr.nei.nih.gov fernando.nodal@physiol.ox.ac.uk novitski@utdallas.edu nykamp@math.umn.edu d.omalley@neu.edu oby@cs.tu-berlin.de

204, 205

210

221

98

182

206

109

192, 231 shani@cns.nyu.edu

Tatsuya.Ohyama@uth.tmc.edu okada@k.u-tokyo.ac.jp iolasaga@wellesley.edu caolman@umn.edu omori@mns.k.u-tokyo.ac.jp ogergo@sunserv.kfki.hu jake.ormond@utoronto.ca oster@math.utah.edu otazu@cshl.edu oviedo@cshl.edu liam@stat.columbia.edu cmparisi@uwaterloo.ca parra@ccny.cuny.edu carl.parsons@newcastle.edu.au naamap@alice.nc.huji.ac.il

222

188

78, 115, 143

107

223

143

224

178

183

225

46

71

112

226

98

207 jp2063@columbia.edu barak@cs.nuim.ie ypeng@mail.sis.pitt.edu andiraperez@hotmail.com

91

137, 226

184

182

Laurent.Perrinet@incm.cnrs-mrs.fr 152 bark5949@yahoo.com 182 bijan@nyu.edu 30 michael_pesavento@urmc.rochester.edu 144

Dumitru.Petrusca@cern.ch 25, 162 jean-pascal.pfister@epfl.ch 185 pillow@gatsby.ucl.ac.uk 153 david_pinto@urmc.rochester.edu 144 pitkow@fas.harvard.edu plenzd@mail.nih.gov

154

214 tp@ai.mit.edu 116 alonpol@techunix.technion.ac.il 39 alex@bcs.rochester.edu pierre.pouget@vanderbilt.edu

47, 75

128 sprescott@salk.edu 145 nicholas.price@anu.edu.au 127 s.prince@cs.ucl.ac.uk astrid.prinz@emory.edu pritched@mit.edu apuckett@utdallas.edu

135

208

85

182 gopathy@uchicago.edu steve@hss.caltech.edu

242

121 rodri@vis.caltech.edu 70 ithai@lobster.ls.huji.ac.il 9 ranades@cshl.edu jeffrey.d.schall@vanderbilt.edu dutch@retina.anatomy.upenn.edu

33

172

128

58, 155 stephanie.ratte@mail.mcgill.ca

J.C.A.Read@ncl.ac.uk j-rebesco@northwestern.edu preinagel@ucsd.edu

Alexandre.Reynaud@incm.cnrs-mrs.fr

145

156

157

158

152 bjr@ln.nimh.nih.gov 85, 89 hrikimar@mail.doshisha.ac.jp 35 jritt@mit.edu carson_roberts@yahoo.com rohrkemper@gmail.com

Edmund.Rolls@psy.ox.ac.uk

227

146

228

57

25

Walter Senn

Thomas Serre

Sebastian H Seung

Ben Seymour

Michael N Shadlen

Reza Shadmehr

Maoz Shamir

Shihab Shamma

Ladan Shams

Jitendra Sharma

Tatyana Sharpee

Jonathan Shaw

Alexander Sher

Denis Sheynikhovich

LieJune Shiau

Yury P Shimansky

Stuart Shipp

Jonathon Shlens

Athanassios Siapas

Thanos G Siapas

Hava Siegelmann

Eero P Simoncelli

Tania Singer

Frances K Skinner

Stelios M Smirnakis

Evan C Smith

Max Snodderly

Sara A Solla

Samuel G Solomon

Haim Sompolinsky

Edward Soucy

Martin A Spacek

Ranulfo Romo

Andrew F Rossi

Arnd Roth

Constantin A Rothkopf

Stefan Rotter

Yasser Roudi

Alexander Roxin

Jonathan E Rubin

Michele Rucci

William S Ryu

Maneesh Sahani

Paul Sajda

Yutaka Sakai

Emilio Salinas

Daniel Salzman

Aravinthan Samuel

Fernanda Saraga

Massimo Scanziani

Jeff Schall

Jeffrey D Schall

Jackie Schiller

Denis Schluppeck

Robert Schmidt

Dietmar Schmitz

Mark J Schnitzer

Jan WH Schnupp

David Schoppik

Christoph E Schreiner

James Schummers

Lars Schwabe

Idan Segev

Ronen Segev

Eyal Seidemann


Nelson Spruston

Lakshminarayan Srinivasan

Arun P Sripati

Garrett B Stanley

Armen Stepanyants

Greg J Stephens

Peter Sterling

Klaus M. Stiefel

Alan A Stocker

Mark Stopfer

Thomas Strosslin

Kristy A Sundberg romo@ifc.unam.mx andrew.rossi@vanderbilt.edu arnd.roth@ucl.ac.uk crothkopf@cvs.rochester.edu rotter@biologie.uni-freiburg.de yasser@gatsby.ucl.ac.uk alexander.roxin@upf.edu rubin@math.pitt.edu rucci@cns.bu.edu wsryu@princeton.edu maneesh@gatsby.ucl.ac.uk ps629@columbia.edu sakai@inter7.jp esalinas@wfubmc.edu cds2005@columbia.edu samuel@physics.harvard.edu fernanda.saraga@utoronto.ca jeffrey.d.schall@vanderbilt.edu jackie@techunix.technion.ac.il denis@cns.nyu.edu r.schmidt@biologie.hu-berlin.de dietmar.schmitz@charite.de

243

229

174

159

91

105, 196

210

13

88

128

73

173

72

209

80

195

15

198

27

128

39

222

118

118 jan.schnupp@physiol.ox.ac.uk junk@schoppik.com chris@phy.ucsf.edu schummej@mit.edu schwabe@cs.tu-berlin.de idan@lobster.ls.huji.ac.il

RSegev@molbio.Princeton.EDU eyal@mail.cps.utexas.edu terry@salk.edu wsenn@cns.unibe.ch serre@ai.mit.edu seung@mit.edu bseymour@fil.ion.ucl.ac.uk

230

197

12, 140

231

136

9

69

126

145, 147, 199

187

116

23, 87, 186

175 shadlen@u.washington.edu 177 reza@bme.jhu.edu 18 shamir@bu.edu sas@umd.edu

160

77 ladan@psych.ucla.edu jeetu@mit.edu sharpee@phy.ucsf.edu jshaw@cs.rochester.edu

121

231

31, 220, 232

161 sasha@scipp.ucsc.edu denis.sheynikhovich@epfl.ch shiau@cl.uh.edu yury.shimansky@asu.edu s.shipp@ucl.ac.uk shlens@salk.edu siapas@caltech.edu thanos@caltech.edu

25, 162

50

211

94

135

162

238

26 hava@cs.umass.edu eero@cns.nyu.edu t.singer@fil.ion.ucl.ac.uk fskinner@uhnresearch.ca

228

153, 236

175

210 stelios.smirnakis@tuebingen.mpg.de 26 evan@cnbc.cmu.edu 233 msnodderly@mcg.edu solla@northwestern.edu

234

157 samuels@medsci.usyd.edu.au 240

10, 96, 154, 186, 218 soucy@mcb.harvard.edu 238 mspacek@interchange.ubc.ca 235 spruston@northwestern.edu ls2@neurostat.mgh.harvard.edu sparun@cnbc.cmu.edu gstanley@deas.harvard.edu

42

163

122

48, 216 a.stepanya@neu.edu gstephen@princeton.edu peter@retina.anatomy.upenn.edu stiefel@salk.edu alan.stocker@nyu.edu stopferm@mail.nih.gov thomas.strosslin@a3.epfl.ch sundberg@salk.edu

212

198

58, 69, 155

147

236

111

50

237

26

Jonathan D Victor

Renan Vitral

Horatiu Voicu

Michael T Wahl

Pascal Wallisch

Kenneth Whang

Kuan Hong Wang

Xiao-Jing Wang

Yoshiaki Watanabe

Roxanna M Webber

Mike Wehr

Chong Weng

Daniel Z Wetmore

Theodore G Weyand

Olivia L White

Louise Whiteley

Jim Wielarrd

Charles J Wilson

Robert C Wilson

Klaus Wimmer

Kong-Fatt Wong

Melanie A Woodin

Brian D Wright

Xundong Wu

Zhiyong Yang

Chun-I Yeh

Wako Yoshida

Eric D Young

Joshua M Young

Angela J Yu

Anthony M Zador

Hatim A Zariwala

Riccardo Zecchina

Richard Zemel

Kechen Zhang

Tao Zhang

Junmei Zhu

Leo van Hemmen

Mriganka Sur

Kelly J Suter

Karel Svoboda

Nicholas V Swindale

Tobi Szuts

Lung-Hao Tai

Chris Tailby

Hirokazu Tanaka

David W Tank

Jordan A. Taylor

Josh B Tenenbaum

Jun-nosuke Teramae

David Terman

Pramodsingh H Thakur

Tara C Thiagarajan

Kurt A. Thoroughman

Kay Thurley

Paul H Tiesinga

Naftali Tishby

Anne-Elise Tobin

Emanuel Todorov

Andreas S Tolias

Susumu Tonegawa

Elizabeth B Torres

Taro Toyoizumi

Alessandro Treves

Bryan P Tripp

Wilson Truccolo

Srinivas C Turaga

Douglas B Tweed

Naoshige Uchida

Leslie Ungerleider

Martin Usrey

Eleni Vasilaki msur@mit.edu ksuter@LearnLink.Emory.Edu

189, 231

146

8 swindale@interchange.ubc.ca 235 szuts@fas.harvard.edu ltai@cshl.edu

238

239 ct@cns.nyu.edu hirokazu@salk.edui dwtank@princeton.edu jat4@cec.wustl.edu

240

199

107

103 jbt@mit.edu teramae@brain.riken.jp terman@math.ohio-state.edu pramod@jhu.edu tarat@mail.nih.gov thoroughman@biomed.wustl.edu k.thurley@biologie.hu-berlin.de

18

213

195

164

214

103

118 tishby@cs.huji.ac.il atobin@brandeis.edu todorov@ucsd.edu andreas.tolias@tuebingen.mpg.de tonegawa@mit.edu etorres@vis.caltech.edu taro@sat.t.u-tokyo.ac.jp ale@sissa.it bptripp@engmail.uwaterloo.ca

Wilson_Truccolo@Brown.edu sturaga@mit.edu douglas.tweed@utoronto.ca uchida@cshl.edu wmusrey@ucdavis.edu vasilaki@cns.unibe.ch

207

148

16, 108

26

189

200

165

209

215

201

186

102, 104, 202

84, 166, 180

38

54

187 jdvicto@med.cornell.edu renan@icb.ufjf.br horatiu@voicu.us mwahl@berkeley.edu

59, 167

176

188

168 wallisch@uchicago.edu 242

20 wangkh@mit.edu xjwang@brandeis.edu

189

79,177,187 kwatanab@mail.doshisha.ac.jp webber@fas.harvard.edu wehr@uoregon.edu cweng@sunyopt.edu wetmore@stanford.edu tweyan@lsuhsc.edu white.olivia@gmail.com l.whiteley@ucl.ac.uk

35

216

217

48

190

54

218

243 djw21@columbia.edu charles.wilson@utsa.edu rcwilson@seas.upenn.edu klaus@cs.tu-berlin.de kfwong@brandeis.edu mwoodin@zoo.utoronto.ca bdwright@phy.ucsf.edu xundongw@usc.edu yang@mail.cps.utexas.edu cyeh@sunyopt.edu wako-y@is.naist.jp eyoung@bme.jhu.edu josh@cs.tu-berlin.de ajyu@princeton.edu zador@cshl.edu zariwala@cshl.edu zecchina@ictp.it zemel@cs.toronto.edu kzhang4@jhem.jhmi.edu tzhang@ucdavis.edu junmeizhu@gmail.com lvh@tum.de

229

40

169

231

177

178

170

191

126

48

179

49

192

244

46, 133, 225, 239

180

193

21

6

245

219

194

27

Cosyne Workshops

The Canyons

Park City, Utah

Thursday, March 9

7:30 – 8:30 AM

8:30 – 11:30 AM

4:30 – 7:30 PM

Full Breakfast

Workshops

Hot and cold beverages – Grand Ballroom Lobby

Workshops continue


Kokopelli Parlor II

Parlor rooms (see below)


Functional architectures and neuronal computations in the prefrontal cortex

Organized by: Etienne Koechlin, Gregor Rainer, and Xiao-Jing Wang

Models of multisensory integration: psychophysical and neural constraints

Organized by: Virginie van Wassenhove, Ladan Shams, and John Jeka

Kokopelli Parlor III

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

Advances in activity-dependent plasticity.

Organized by: Paul Munro

Arrowhead Parlor I

Kokopelli Parlor I

28

Friday, March 10

7:30 – 8:30 AM

8:30 – 11:30 AM

4:30 – 7:30 PM

8:00 – 11:30 PM

Full Breakfast

Workshops


Workshops continue


Banquet

Kokopelli Parlor II



Kokopelli Grand Ballroom

Functional architectures and neuronal computations in the prefrontal cortex (continues)

Organized by: Etienne Koechlin, Gregor Rainer, and Xiao-Jing Wang

Difficult issues in auditory scene analysis

Organized by: Barbara Shinn-Cunningham and Shihab Shamma

Neural and behavioral variability: nuisance or necessity?

Organized by: Leslie Osborne and Philip Sabes

Computing with spikes: more than spike-counts - every spike counts?

Sophie Deneve, Boris Gutkin, and Mate Lengyel

The role of natural images in guiding our understanding of visual function

Organized by: Nicole Rust, Jonathan Pillow, and Eero Simoncelli

Genetic approaches for system neuroscience

Organized by: Gero Miesenboeck and Susana Lima

Parietal cortex: function and computations

Organized by: Jennifer Groh

Kokopelli Parlor III

White Pine Parlor I

Arrowhead Parlor I

Arrowhead Parlor II

Kokopelli Parlor I

Painted Horse II

White Pine Parlor II

29

Functional architectures and neuronal computations in the prefrontal cortex

Etienne Koechlin 1 , Gregor Rainer 2 , and Xiao-Jing Wang 3

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

9:50 - 10:10 Break

10:10 - 10:40 John O'Doherty (Caltech)

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

Abstract state-based inference in human ventromedial prefrontal cortex during reward-based decision making

The role of fluctuations in decision-making

4:30 - 5:00 Matthew Rushworth (Oxford)

5:10 - 5:40 Michael Colombo (Dunedin)

5:50 - 6:10 Break

6:10 - 6:40 Nicolas Brunel (Paris)

Contrasting the roles of the medial and lateral prefrontal cortices in decision-making

Neural correlates of executive control in the avian "prefrontal cortex

Scenarios for persistent activity in cortical network models

Short- and long-term reward prediction in cortico-basal ganglia loops

Gaps in the schedule are for questions/discussion.

30

Schedule continues (Friday)

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

9:50 - 10:10 Break

10:10 - 10:40 Aldo Genovesio (NIH)

Cooperation between prefrontal and visual cortex during working memory

Representation of strategies and goals in the prefrontal cortex

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

Slow reverberatory cortical dynamics underlying cognition

5:50 - 6:10 Break

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

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


31

Models of multisensory integration: psychophysical and neural constraints

Virginie van Wassenhove 1,2 , Ladan Shams 1 , and John Jeka 3

1

UCLA,

2

Caltech,

3


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?


8:30 - 8:35 Opening remarks

9:45 - 10:10 Break

10:10 - 10:35 Phillip Sabes (UCSF)

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

Multisensory integration in MSTd: Reference frames and correlations with behavior

See, feel, and learn: sensory integration and adaptation

10:45 - 11:05 Virginie van Wassenhove (UCLA, Caltech)

11:15 - 11:30 Discussion

4:30 - 4:55 Alex Pouget (University of Rochester)

5:05 - 5:30 Tom Anastasio (University of Illinois)

5:30 - 6:00 Break

Analysis-by- synthesis: hearing and seeing speech that is being produced

Neural basis of Bayes-optimal mutlisensory integration: theory and experiments

Testing Models of Multisensory Integration

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

6:35 - 7:30 Concluding Remarks and Discussion


32

Adaptation: neural, psychological, and computational aspects

Odelia Schwartz 1 , Colin Clifford 2 , and Peter Dayan 3

1

Salk Institute,

2

University of Sydney,

3

Gatsby 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.


8:30 - 9:00 Odelia Schwartz (Salk Institute)

9:10 - 9:40 Markus Meister (Harvard University)

9:50 - 10:10 Break

Adaptation and natural image statistics

Neural mechanisms for predictive coding in the retina

10:50 - 11:20 Adrienne Fairhall (University of Washington)

4:30 - 5:00 Colin Clifford (The University of Sydney)

Adaptation to mean and variance in the primate visual system

Adaptation in cortical neurons

5:10 - 5:40 Garrett Stanley (Harvard University)

5:50 - 6:10 Break

6:10 - 6:40 Alan Stocker (New York University)

Self-calibration in sensory coding

Adaptive mechanisms for enhancing neural encoding in sensory pathways

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


33

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

David Heeger

NYU

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.


8:30 - 9:00 David Heeger (NYU)

9:45 - 10:00 Break

10:45 - 11:15 Justin Gardner (NYU)

The next generation of functional magnetic resonance imaging

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

Decoding conscious and unconscious perception from dynamic brain patterns

High spatial resolution imaging to determine the dependence on spatial scale of classifier performance

Analysis of multi-voxel patterns of response to faces and objects

6:00 - 6:15 Break

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

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


34

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.


8:30 - 8:45 Introduction

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

Discriminating temporal patterns: spiking neurons and 'ideal observers'

Temporal coding of time varying stimuli

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


35

Models of model systems

Anne-Elise Tobin and Adam Taylor


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.



10:15 - 10:35 Break

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

Modeling neuromuscular modulation in Aplysia


5:25 - 6:00 Astrid Prinz (Emory University)

6:10 - 6:30 Break

Lessons about neural system robustness and homeostasis from the crustacean stomatogastric ganglion

Neural processing of optic flow in the fly


36

Advances in activity-dependent plasticity

Paul Munro


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 ratebased 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-timingdependent 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!).


8:30 - 8:45 Welcome

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

Novel features of synaptic plasticity induced by natural spike patterns

9:35 - 9:50 Break

External application of STDP: a computer simulation


Modular competition driven by nmda receptor subtypes in spike-timingdependent plasticity

Modular competition driven by nmda receptor subtypes in spike-timingdependent plasticity

5:20 - 5:45 Break

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

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


37

Difficult issues in auditory scene analysis

Barbara Shinn-Cunningham 1 and Shihab Shamma 2

1

Boston University,

2


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

8:40 - 8:55 Rhodri Cusack (MRC, Cambridge)

9:00 - 9:15 Erv Hafter (UC Berkeley)

Announcement

Introduction, Extreme Position

Statement

General Griping

Attention and source segregation

Point

Counterpoint

Cindy Moss (University of Maryland College Park)

10:00 - 10:30 Break

10:30 - 10:45 Daniel Pressnitzer (Ecole Normale Superieure)

10:50 - 11:05 Sue Denham (University of Plymouth)

11:10 - 11:30 Christophe Micheyl (MIT)

Pierre Divenyi (EBIRE, Martinez)

4:30 - 4:45 Steve Colburn (Boston University)

4:50 - 5:05 Chris Darwin (University of Sussex)

5:10 - 5:45 Rich Stern (Carnegie Mellon University)

David McAlpine (UCL)

5:45 - 6:15 Break

6:15 - 6:30 Bill Yost (Parmly Hearing Institute, Loyola University)

6:35 - 6:50 Andy Oxenham (University of Minnesota)

6:55 - 7:30 Ginny Richards (University of Pennsylvania)

Doug Brungart (Air Force Research Lab)

Discussion

Bistability and source segregation

Point and Counterpoint

Discussion

Spatial hearing and source segregation

Point

Counterpoint

Discussion

Information masking and source segregation

Point

Counterpoint

Discussion


38

Neural and Behavioral variability: nuisance or necessity?

Leslie Osborne and Philip Sabes

UCSF

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

External noise, internal noise and neural computation

9:15 - 9:35 Mike DeWeese (CSHL)

9:45 - 10:15 Break

10:15 - 10:35 Leslie Osborne (UCSF)

10:45 - 11:05 Emo Todorov (UCSD)

How much cortical variability is really noise?

Is behavior as noisy as you think it is?

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)

5:30 - 6:00 Break

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

6:30 - 6:50 Philip Sabes (UCSF)

Random thoughts on the source and role of cortical variability

Variability, coordinate transforms, and learning


39

Computing with spikes: more than spike-counts - every spike counts?

Sophie Deneve 1 , Boris Gutkin 2 , and Mate Lengyel 3

1

CNRS Lyon,

2

CNRS and the Pasteur Institute,

3


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 "spikecounts". 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)


Recursive Bayesian estimation in population codes

9:30 - 10:00 Break

10:00 - 10:30 Yves Fregnac (CNRS)

10:40 - 11:20

Peter Latham (Gatsby

Computational Neuroscience Unit,

UCL)

Requiem for a spike

Time precision of the spiking behaviour of V1 cortical neurons is optimally adapted to the complexity of natural visual scenes

5:10 - 5:40 Break


Spikes propagating beliefs

Distributed Phase Codes


40

The role of natural images in guiding our understanding of visual function

Nicole Rust 1 , Jonathan Pillow 2 , and Eero Simoncelli 1

1

NYU,

1


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)

9:10 - 9:40 Gidon Felsen (CSHL)

9:50 - 10:10 Break

10:10 - 10:40 Vincent Bonin (Smith-Kettlewell)

10:50 - 11:20 Jack Gallant (UC Berkeley)

4:30 - 5:00 Eero Simoncelli (HHMI and NYU)

5:10 - 5:40 Pamela Reinagel (UCSD)

5:50 - 6:10 Break

6:10 - 6:40 Bill Geisler (UT Austin)

In praise of artifice

Complex cell response sensitivity depends on image statistics

Dynamic gain control in the responses of lateral geniculate neurons to complex stimuli

Using system identification and natural images to reveal new principles of neuronal coding

Using models of natural images to understand visual function

Thalamic bursting in response to natural stimuli

Bayesian natural scene statistics

Natural scene statistics and the organization of the retina


41

Genetic approaches for systems neuroscience

Gero Miesenboeck 1 and Susana Lima 2

1

Yale,

2

CSHL

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

9:40 -10:00 Break

10:00 - 10:20 Florian Engert (Harvard)

10:30 - 10:50 Baron Chanda (UCLA)

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

Visual processing in the developing zebra fish

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

Oxygen sensing and the evolution of foraging behavior in C. elegans

5:00 - 5:20 Herwig Baier (UCSF)

6:00 - 6:30 Break

6:30 - 6:50 Bruce Baker (Stanford)

Visual perception: From gene to synapse to circuit to behavior in zebrafish

Genes modulating selective attention and associated Local Field

Potentials in Drosophila

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


42

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?

9:15 - 9:45 Break

Schedule (Friday)

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


5:00 - 5:20 Bijan Pesaran (NYU)

5:30 - 6:00 Break


Multimodal motor feedback to the lateral intra parietal area

Optimal spatial representations in the parietal cortex

Dorsal premotor neurons encode the relative position of the hand and eye during reach planning

Auditory and visual references in the intraparietal sulcus


43

Notes

44

45

46

Final program book

COSYNE

Computational and Systems Neuroscience

2006

Main Meeting

March 5-8

Salt Lake City, Utah

Workshops

March 9-10

Park City, Utah

Table of Contents

Notes on talks

Food included with registration

Additional Credits

Schedule Overview

Cosyne Main Meeting

Cosyne Workshops

Cosyne Main Meeting

Marriott–Downtown

Salt Lake City, Utah

Sunday, March 5

Monday, March 6

Tuesday, March 7

Wednesday, March 8

Poster presentations, Session I

Poster presentations, Session II

Author Index (main meeting only)

Cosyne Workshops

The Canyons

Park City, Utah

Thursday, March 9

Friday, March 10

UCLA,

Caltech,

University of Maryland College Park

Salk Institute,

University of Sydney,

Gatsby Computational Neuroscience Unit

NYU

Boston University

Brandeis University

University of Pittsburgh

Boston University,

University of Maryland College Park

UCSF

CNRS Lyon,

CNRS and the Pasteur Institute,

Gatsby Computational Neuroscience Unit

NYU,

Gatsby Computational Neuroscience Unit

Yale,

CSHL

Dartmouth College

Notes

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