Poster Session I
8:30pm Thursday 26th February
I-1. Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye M. Juusola, A. Nikolaev, T. J. Wardill, C. J. OKane, G. G. de Polavieja, L. Zheng, University of Sheffield
I-2. Offset adaptation in the inner hair cell synapses enhances speech coding H. Wang, M. Rudnicki, W. Hemmert, Infineon Technologies, AG
I-3. Phase Response Curve of Spike Response Model M. IIDA, T. Omori, T. Aonishi, M. Okada, Grad. sch. of Front. Sci., The Univ. of Tokyo
I-4. Intrinsic membrane properties control gamma-frequency input integration S. Otte, A. Hasenstaub, T. Sejnowski, E. Callaway, Salk Institute
I-5. The spatial extent of attentional facilitation and inhibition of return in humans and monkeys A. Khan, N. Takahashi, S. Heinen, R. McPeek, Smith-Kettlewell Eye Research Institute
I-6. Saccade related phase resetting of theta and delta rhythms modulates cortical high gamma activity C. Kovach, H. Kawasaki, N. Tsuchiya, M. Howard, R. Adolphs, University of Iowa Hospitals and Clinics
I-7. Causal role of auditory cortex in mediating attention to moments in time S. Jaramillo, A. Zador, Cold Spring Harbor Laboratory
I-8. Computing the cost function in decision making J. Drugowitsch, R. Moreno-Bote, A. Pouget, Department for Brain & Cognitive Sciences
I-9. Estimation and reproduction of time intervals by LIP neurons M. Jazayeri, M. Shadlen, University of Washington
I-10. Behavior-dependent responses in primate frontal cortex neurons during natural behavior C. Miller, X. Wang, Johns Hopkins University
I-11. Adaptive decision making in monkeys during a non-stationary rock-paper-scissors game H. Abe, D. Lee, Yale University School of Medicine
I-12. The Human Brain Computes Two Different Prediction Errors J. Glascher, N. Daw, P. Dayan, J. O'Doherty, California Instititute of Technology
I-13. A Computational and Neurobiological Account of Theory of Mind W. Yoshida, B. Seymour, K. Friston, R. Dolan, University College London
I-14. Bayesian decision making predicts adaptive sensory weights and decision threshold S. Deneve, Ecole Normale Supérieure, Paris
I-15. Computational implications of a normalized value representation in decision circuits K. Louie, L. Grattan, P. Glimcher, New York University
I-16. Analysis of neural activity during error trials in decision-making task R. J. Cotton, A. S. Tolias, Baylor College of Medicine
I-17. Evaluating signal detection models of perceptual decision confidence B. Maniscalco, H. Lau, Columbia University
I-18. Dynamical state spaces of cortical networks representing various horizontal connectivities N. Voges, L. Perrinet, INCM / CNRS Univ. Aix-Marseille
I-19. Typical behaviors in co-evolving recurrent network of oscillatory neurons T. Aoki, T. Aoyagi, Kyoto Univesity
I-20. A constructive mean-field analysis of multi population neural networks with random synaptic weights O. Faugeras, J. Touboul, B. Cessac, INRIA
I-21. Insights into Parkinson's disease from mean-field modeling of brain electrical activity S. van Albada, R. Gray, P. Drysdale, P. Robinson, The University of Sydney
I-22. Neural correlations in a heterogeneous network model dominated by recurrent inhibition A. Bernacchia, X.-J. Wang, Yale University
I-23. Generalized Wilson-Cowan rate equations for correlated activity in neural networks. M. Buice, J. Cowan, C. Chow, National Institutes of Health
I-24. A role for symmetric head-angular-velocity cells: Tuning the head-direction network. P. Stratton, G. Wyeth, J. Wiles, The University of Queensland
I-25. Improved (I)CA-noise elimination of electrophysiological data using band-pass filtered components K. Görgen, C. Bosman, T. Womelsdorf, R. Oostenveld, P. Fries, BCCN Berlin
I-26. State Dependent Frequency Modulation of Hippocampal Theta Activity D. Nguyen, M. Wilson, E. Brown, R. Barbieri, Massachusetts General Hospital
I-27. Complex Bayesian Inference in Neural Circuits using Divisive Normalization J. Beck, P. Latham, A. Pouget, Gatsby Computational Neuroscience Unit, UCL
I-28. Neural model of action-selective neurons in STS and area F5 M. Giese, A. Casile, F. Fleischer, Hertie Institute for Clinical Brain Research
I-29. Deciding without remembering S. Ganguli, R. Guetig, H. Sompolinsky, UCSF
I-30. Anthropic correction for mutual information and its application to neural redundancy estimations. F. Theunissen, M. Gastpar, P. Gill, S. Munro, University of California, Berkeley
I-31. Separation of single neurons from optical recordings in Tritonia diomedea using ICA C. Moore-Kochlacs, E. Hill, W. N. Frost, J. Wang, T. Sejnowski, Salk Institute for Biological Studies
I-32. Towards linking blood flow and neural activity : Petri net-based energetics model for neurons V. Nagarajan, A. Mohan,
I-33. Task-driven Saliency Using Natural Statistics (SUN) M. Tong, C. Kanan, L. Zhang, G. Cottrell, University of California, San Diego
I-34. Predictors of successful memory encoding in the human hippocampus and amygdala U. Rutishauser, A. N. Mamelak, I. B. Ross, E. Schuman, California Institute of Technology
I-35. Topological stability of the hippocampal spatial map Y. Dabaghian, F. Memoli, G. Singh, L. Frank, G. Carlsson, Keck Center for Integrative Neuroscience
I-36. Dynamic hippocampal remapping using recurrent inhibition on realigning grid cell inputs J. Monaco, L. Abbott, Center for Theoretical Neuroscience, Columbia
I-37. Synaptic decision making: flipping switch-like synapses with cubic autocatalysis. G. Wittenberg, Siemens Corporate Research
I-38. Projection Neurons in Medial and Lateral Striatum Show Different Ensemble Patterns during Learning C. Thorn, A. Graybiel, Massachusetts Institute of Technology
I-39. Frequency selectivity using spike-timing-dependent plasticity M. Gilson, M. Buerck, A. Burkitt, J. L. van Hemmen, The University of Melbourne
I-40. Biologically plausible model of synapse formation in the neocortex G. Escobar, A. Stepanyants, Northeastern University
I-41. Associative representations in lateral intraparietal (LIP) area J. Fitzgerald, J. Assad, D. Freedman, Harvard Medical School
I-42. Specificity versus associativity in models of paired-stimulus learning M. Bourjaily, P. Miller, Volen Center for Complex Systems
I-43. Sensory input balances excitation and inhibition to close the auditory cortical critical period A. Dorrn, C. Schreiner, R. Froemke,
I-44. The attention-gated reinforcement learning model: performance and predictions. L. Watling, P. Roelfsema, A. Van Ooyen, CNCR, VU University Amsterdam
I-45. Neural activity in nigrostriatal circuits can signal action value and action sequence X. Jin, R. Costa, Lab for Integrative Neuroscience, NIAAA/NIH
I-46. Value function uncertainty as a cognitive map for reinforcement learning E. Chastain, N. Daw, Computer Science, University of Washington
I-47. Effects of learning on the motor gestures of birdsong J. Mendez, A. DallAsén, B. Cooper, F. Goller, Department of Biology, University of Utah
I-48. Context dependent movement encoding and variability in the motor cortex M. Nawrot, J. Rickert, A. Riehle, A. Aertsen, S. Rotter, Freie Universitat Berlin
I-49. Can divergent connectivity generate reliable sparse activity patterns? T. Nowotny, R. Huerta, University of Sussex
I-50. Bifurcation analysis of neural mass equations R. Veltz, O. Faugeras, INRIA Sophia Antipolis, NeuroMathComp Team
I-51. Selective in vivo activation of fast- or regular-spiking barrel cortex neurons with Channelrhodopsin J. Cardin, M. Carlen, K. Meletis, U. Knoblich, F. Zhang, K. Deisseroth, L.-H. Tsai, C. Moore, McGovern Institute, MIT
I-52. Stimulus onset quenches neural variability: a widespread cortical phenomenon M. Churchland, B. Yu, J. Cunningham, L. Sugrue, M. Cohen, G. Corrado, W. Newsome, A. Clark, P. Hosseini, B. Scott, EE Dept., Stanford University
I-53. Columnar coding of neuronal populations in primary visual cortex R. Herikstad, J. Baker, C. Gray, S.-C. Yen, National University of Singapore
I-54. A multivariate phase distribution and its estimation C. Cadieu, K. Koepsell, University of California, Berkeley
I-55. Towards a physiological exploration of sensorimotor processing in behaving Drosophila J. Seelig, E. Chiappe*, G. Lott, T. Adelman, M. Reiser, V. Jayaraman, Janelia Farm Research Campus
I-56. In what regimes do regular-spiking excitatory neurons drive gamma oscillations? D. Vierling-Claassen, J. Cardin, C. Moore, S. Jones, MGH-MIT-HMS Martinos Cntr for Biomed. Imaging
I-57. Perception as Modeling: A neural network that extracts and models predictable elements from input. D. Sussillo, L. Abbott, Columbia University
I-58. Modelling Adaptive Coding of Sound Localization Cues in the Inferior Colliculus. P. Hehrmann, J. Maier, N. Harper, D. McAlpine, M. Sahani, Gatsby Computational Neuroscience Unit, UCL
I-59. Quantitative analysis of the learning dynamics of a two-phase neural model of classical conditioning I. Herreros-Alonso, A. Giovannucci, R. Zucca, M. Mintz, P. F. M. J. Verschure, SPECS. Universitat Pompeu Fabra
I-60. Possible differential sensitivity to the matrix vs core thalamocortical systems by MEG vs EEG N. Dehghani, E. Halgren, S. Cash, MGH, Neurology Dep. Harvard
I-61. High-performance halorhodopsin variants for improved genetically-targetable optical neural silencing B. Chow, X. Han, X. Qian, M. Li, E. Boyden, MIT Media Lab
I-62. Adaptation in simple neurons: dependence of feature selectivity on stimulus statistics M. Famulare, A. Fairhall, University of Washington
I-63. Decoding of stimulus velocity using a model of ganglion cell populations in primate retina. E. Lalor, Y. Ahmadian, J. Pillow, E. Simoncelli, L. Paninski, Trinity College Dublin
I-64. Decoding dynamic patterns of neural activity using a biologically plausible fixed set of weights E. Meyers, D. Freedman, G. Kreiman, E. Miller, T. Poggio, Massachusetts Institute of Technology
I-65. A decoder-based spike train metric for analyzing the neural code in the retina Y. Ahmadian, J. Pillow, J. Shlens, E. Simoncelli, E. Chichilnisky, L. Paninski, Columbia University
I-66. Precise spike synchronization in the gamma band increases information gain in awake monkey V1 T. Womelsdorf, B. Lima, M. Vinck, R. Oostenveld, W. Singer, S. Neuenschwander, P. Fries, Donders Institute f.Brain,Cognition&Behavior
I-67. Temporal processing with plastic short term synaptic dynamics R. Guetig, H. Sompolinsky, M. Tsodyks, Racah Institute of Pysics
I-68. Which model can properly describe dynamics and smoothness of firing rate? K. Takiyama, K. Katahira, M. Okada, The University of Tokyo.
I-69. How far the decoding process in the brain can be simplified? M. Oizumi, T. Ishii, K. Ishibashi, T. Hosoya, M. Okada, The University of Tokyo
I-70. Extensions to copula based modeling of spike counts A. Onken, S. Grünewälder, K. Obermayer, Berlin Institute of Technology
I-71. Decoding of regular Purkinje cell spiking based on synaptic depression in a model of a DCN neuron J. Luthman, R. Maex, R. Adams, N. Davey, V. Steuber, University of Hertfordshire
I-72. Overcoding-and-paring: a bufferless neural chunking model G. Rinkus, Brandeis University
I-73. Computational models of millisecond level neuronal timing mechanisms B. Aubie, S. Becker, P. Faure, McMaster University
I-74. Temporal response properties in auditory cortex are layer-dependent G. B. Christianson, M. Sahani, J. Linden, UCL Ear Institute
I-75. Targeting auditory cortex: combining physiology and anatomy to identify higher auditory regions P. Crum, E. Issa, T. Hackett, X. Wang, Johns Hopkins School of Medicine
I-76. Linking stimulus response properties and functional circuitry in the mouse auditory cortex H. Oviedo, I. Bureau, K. Svoboda, A. Zador, Cold Spring Harbor Laboratory
I-77. Temporal coding in the olfactory bulb of awake behaving rats during active sampling. K. Cury, N. Uchida, Harvard University
I-78. Receptive field size and spike threshold control decoding of information from synchronous afference J. Middleton, A. Longtin, J. Benda, L. Maler, Department of Neurobiology
I-79. Receptive field maps depend on high order stimulus structure: evidence for nonlinear feedback J. Victor, F. Mechler, A. Schmid, I. Ohiorhenuan, K. Purpura, Weill Medical College of Cornell
I-80. Learning Natural Image Structure with a Horizontal Product Model U. Köster, J. Lindgren, A. Hyvärinen, University of Helsinki
I-81. A point process model for the parabigeminal nucleus as a recursive estimator R. Ma, T. Coleman, J. Malpeli, Coordinated Science Laboratory
I-82. Inference of object attributes from local image features caused by occlusion X. Pitkow, Columbia University
I-83. Sensory input statistics and network mechanisms in primate primary visual cortex P. Berens, J. Macke, A. Ecker, R. J. Cotton, M. Bethge, A. Tolias, MPI for Biological Cybernetics
I-84. Modelling of light responses of Drosophila Photoreceptor Z. Song, D. Coca, S. Billings, M. Juusola, University of Sheffield
I-85. Adaptation-induced changes in orientation tuning and tilt aftereffect in network models of V1 K. Wimmer, K. Obermayer, Berlin Institute of Technology
I-86. Roles of feedforward, recurrent and feedback connections in visual processing S. Moldakarimov, M. Bazhenov, T. Sejnowski, The Salk Institute for Biological Studies
I-87. Motion discrimination unimpaired during silencing of binocular disparity information in macaque MT A. Smolyanskaya, R. Born, Harvard Medical School
I-88. Compensating fixational eye movements: A network model Y. Burak, U. Rokni, H. Sompolinsky, M. Meister, Harvard University
I-89. Probing the early visual system with naturalistic, synthetic images R. Coen-Cagli, S. Wissig, A. Kohn, O. Schwartz, Albert Einstein College of Medicine
I-90. Experience-dependent changes in perceptual capacity: behavior and brain states M. Wenger, L. Blaha, J. Townsend, R. Von Der Heide, The Pennsylvania State University
I-91. What is the "contrast" in contrast adaptation? K. Simmons, G. Tkacik, J. Prentice, V. Balasubramanian, University of Pennsylvania
I-92. Adaptation in MT neurons shifts speed tuning laterally, facilitating change discrimination N. Price, R. Born, Neurobiology, Harvard Medical School
I-93. Color Constancy of V1 Double Opponent Cells to Natural Images D. Fisher, B. Conway, M. Goldman, University of California Davis
I-94. Distinct functional populations within macaque area MT as revealed by waveform analysis F. Roemschied, F. Bremmer, B. Krekelberg, Rutgers University
I-95. Divisive normalization provides summation and competition of population responses in visual cortex L. Busse, S. Katzner, A. Benucci, M. Carandini, University College London
I-96. Area MT pattern motion selectivity by integrating 1D and 2D motion features from V1 a neural model C. Beck, H. Neumann, University of Ulm
I-97. Rats' Detection of Oriented Visual Target is Impaired by Collinear Flankers P. Meier, E. Flister, P. Reinagel, UCSD
I-98. Suboptimal selection of initial saccade in a visual search task c. MORVAN, L. Maloney, Psychology & Neural Science, NYU, NY
I-99. Changes of visual response properties in area MT due to eye movements T. Hartmann, T. Albright, F. Bremmer, B. Krekelberg, Rutgers University
I-100. High-speed imaging of local population activity in mouse visual cortex V. Bonin, M. Histed, R. C. Reid, Harvard Medical School
