Poster Session II
7:30pm Friday 27th February
II-1. Predicting spike times of any cortical neuron R. Kobayashi, Y. Tsubo, S. Shinomoto, Kyoto university
II-2. Power-law distributions of inter-spike intervals in in vivo cortical neurons Y. Tsubo, Y. Isomura, T. Fukai, Neural Circuit Theory, RIKEN BSI
II-3. Modeling the Electrical Function of Dendritic Spines T. Vogels, R. Araya, R. Yuste, Columbia University
II-4. Unequal partitioning of AMPA and NMDA conductances leads to robust temporal order sensitivity Y. Wei, B. Mel, University of Southern California
II-5. Reconciling inter-areal gamma-range synchrony with neural irregular activity in selective attention S. Ardid, X.-J. Wang, A. Compte, Yale University School of Medicine
II-6. Spatial attention modulates steady state VEPs in retinotopic human visual cortex T. Lauritzen, A. Wade, University of California, Berkeley
II-7. Estimates of spike-LFP coherence based on finite spiking data vary with mean firing rate J. Curtis, J. Mitchell, J. Reynolds, Salk Institute
II-8. Where to look? Dissociating the effect of reward, salience and attention V. Navalpakkam, C. Koch, A. Rangel, P. Perona, California Institute of Technology
II-9. Category learning and decision making: a cortical circuit model T. Engel, X.-J. Wang, Yale University
II-10. Serotonin modulates choice stickiness through an outcome-independent striatal mechanism. B. Seymour, N. Daw, P. Dayan, J. Roiser, R. Dolan, Wellcome Trust Centre for Neuroimaging
II-11. Computation of value functions based on gains and losses in the cortico-striatal network H. Seo, X. Cai, D. Lee, Yale University School of Medicine
II-12. An optimality framework for understanding inhibitory control in countermanding tasks A. Yu, University of California, San Diego
II-13. A computational theory of prefrontal executive control A. Collins, E. Koechlin, Institut National Santé Et Recherche Médicale
II-14. Active updating of decision boundaries in rats can be explained using bayesian classifiers P. Shenoy, E. Chastain, A. Kepecs, R. Rao, Computer Science, University of Washington
II-15. Competitive acceleration: A surprising consequence of neural decision-making M. Wojnowicz, M. Spivey, M. Ferguson, Cornell University
II-16. Probabilistic population coding of action selection in the basal ganglia E. Kimchi, N. Narayanan, M. Laubach, Yale University School of Medicine
II-17. Structure learning in human sequential decision-making D. Acuna, P. Schrater, Department of Computer Science and Eng.
II-18. Towards inferring neural circuits from population calcium imaging J. Vogelstein, A. Packer, R. Yuste, L. Paninski, Johns Hopkins University
II-19. A neuronal network model for the detection of binary odor mixtures A. Zavada, T. Nowotny, University of Sussex
II-20. Nonlinear identification for modeling and analysis of adaptive neuronal systems U. Friederich, D. Coca, S. Billings, M. Juusola, University of Sheffield
II-21. A Bayesian method to predict the optimal diffusion coefficient in random fixational eye movements D. Pfau, X. Pitkow, L. Paninski, Columbia University
II-22. Temporal memory and network dynamics P. Latham, E. Wallace, Gatsby Computational Neuroscience Unit, UCL
II-23. An analysis of functional connectivity across timescales I. Stevenson, K. Kording, Northwestern University
II-24. Efficient coding of binocular spontaneous activity for innate learning in V1 development M. Albert, D. Field, Cornell University
II-25. Correlation-based learning in resonate-and-fire neurons D. Bouchain, F. Hauser, G. Palm, Ulm University, Germany
II-26. Functional Connectivity between Neuronal Ensembles through Nonlinear Modeling T. Zanos, R. Hampson, S. Deadwyler, T. Berger, V. Marmarelis, University of Southern California
II-27. A Biophysically Inspired Model for Contrast Adaptation Y. Ozuysal, S. Baccus, Stanford University
II-28. Modeling the Temporal Bisection Task in humans and rats. C. Kopec, C. Brody, Princeton University
II-29. Nonlinearity, memory, and phase transitions in animal learning I. Nemenman, Los Alamos National Laboratory
II-30. When can rates be reliably transmitted in feedforward networks? K. Burbank, G. Kreiman, Children's Hospital, Boston
II-31. A very general linear-nonlinear model for the spatio-temporal characterization of visual cells from J. Rapela, G. Felsen, J. Touryan, J. M. Mendel, N. M. Grzywacz, University of Southern California
II-32. Validating a Bayesian model of conflicting sensory inputs R. Natarajan, I. Murray, L. Shams, W. D. Hairston, R. Zemel, University of Toronto
II-33. Sleep as a Monte-Carlo: offline training of grammar-like models of semantic memory in the neocortex F. Battaglia, C. Pennartz, Universiteit van Amsterdam
II-34. What is stored in the hippocampus during tactile discrimination behavior? P. Itskov, E. Vinnik, M. Diamond, International School For Advanced Studies
II-35. The Secret Life of Kernels: Reconsolidation in Flexible memories D. Nowicki, H. Siegelmann, University of Massachusetts Amherst, CS Dept.
II-36. A study on medial temporal lobe online learning neuronal network model with active dendrites X. Wu, B. Mel, University of Southern California
II-37. Tag-Trigger-Consolidation: A model of early and late long-term potentation and depression C. Clopath, L. Ziegler, L. Buesing, E. Vasilaki, W. Gerstner, LCN
II-38. Structural plasticity and memory: Catastrophic forgetting, amnesia, and the spacing effect A. Knoblauch, M.-O. Gewaltig, U. Körner, E. Körner, Honda Research Institute Europe
II-39. Instructive cues for ON-OFF RGC segregation in the LGN of the developing mouse visual system J. Gjorgjieva, S. Eglen, University of Cambridge
II-40. Decision making and perceptual learning for speed discrimination S. Ringbauer, F. Raudies, H. Neumann, University of Ulm
II-41. Optimizing microcircuits through reward modulated STDP P. Joshi, J. Triesch, Frankfurt Institute for Advanced Studies
II-42. Brains strategy for perceptual estimates: model averaging, model selection, or prob. matching? L. Shams, U. Beierholm, UCLA
II-43. TD learning versus motivational salience accounts of dopamine in animal models of OCD T. Toulouse, H. Szechtman, S. Becker, McMaster University
II-44. A spiking network model for learning reward timing in cortex: H. Shouval, J. Gavornik, M. Shuler, Department of Neurobiolgy UT Houston
II-45. The role of frontostriatal circuits in shifting between goal-directed actions and habits C. Gremel, R. Costa, Laboratory for Integrative Neuroscience
II-46. Recurrent neural network modeling of hierarchical motor control and analysis D. Huh, E. Todorov, UCSD Computational Neurobiology
II-47. The interaction of Purkinje cell and Inhibitory Interneuron plasticity during classical conditioning I. Herreros-Alonso, R. Zucca, A. Giovannucci, P. F. M. J. Verschure, SPECS. Universitat Pompeu Fabra
II-48. The relationship between correlations and network states is explained by balanced network dynamics A. Lerchner, P. Latham, Gatsby Computational Neuroscience Unit, UCL
II-49. Analysis of biologically inspired artificial neural networks T. Fares, A. Stepanyants, Northeastern University
II-50. Graph coloring predicts the dynamics of neuronal networks C. Assisi, M. Bazhenov, University of California, Riverside
II-51. Neuronal circuit reconstruction using serial block-face scanning electron microscopy K. Briggman, M. Helmstaedter, T. Euler, W. Denk, Max Planck Institute for Medical Research
II-52. Biologically plausible saliency networks for object recognition S. Han, D. Gao, N. Vasconcelos, University of California, San Diego
II-53. Summation properties of frequency-dependent disynaptic inhibition between pyramidal cells T. Berger, G. Silberberg, R. Perin de Campos, H. Markram, Ecole Polytechnique Federale, Lausanne
II-54. Constructing dopaminergic signals in response to transient inputs in the ventral tegmental area B. Gutkin, M. Graupner, Ecole Normale Supérieure, Paris
II-55. Phase locking of pulse-coupled oscillators with delays is determined by the phase response curve M. Woodman, C. Canavier, LSU Health Sciences Center
II-56. Phase Resetting Curves Predict Network Activity in Networks of Neural Oscillators S. Achuthan, C. Canavier, LSU Health Sciences Center
II-57. On the origin of low frequency fluctuations in the brain resting state E. Hugues, G. Deco, Universitat Pompeu Fabra
II-58. Stimulus space topology vs. network topography in the ring model V. Itskov, C. Curto, Columbia University
II-59. The spatial profile of inhibitory circuitry modulates oscillatory activity in auditory cortex A.-M. Oswald, B. Doiron, J. Rinzel, A. D. Reyes, New York University
II-60. Generation of short-term memory for items and order by unsupervised learning M. Bourjaily, M. Tierz, P. Miller, Volen Center for Complex Systems
II-61. Probing mechanisms of gamma rhythmogenesis with cell type-specific optical neural control G. Talei Franzesi, X. Qian, M. Li, X. Han, C. Borgers, N. Kopell, F. LeBeau, M. Whittington, E. Boyden, Massachusetts Institute of Technology
II-62. Detection of non-stationary higher-order spike correlation H. Shimazaki, S.-i. Amari, E. Brown, S. Gruen, RIKEN Brain Science Institute
II-63. [http://frontiersin.org/conferences/individual_abstract_listing.php?conferid=39&pap=1433 On the cumulants of the spike count distribution of stationary stochastic point processes.] C. van Vreeswijk, CNRS UMR 8119
II-64. Neurons as Monte Carlo Samplers: Sequential Bayesian Inference in Spiking Neural Populations H. Yanping, R. Rao, University of Washington
II-65. Sparse and Invariant Representations of Multi-component Odors in the Mushroom Body K. Shen, S. Tootoonian, A. Narayan, G. Laurent, California Institute of Technology
II-66. Distinct adaptive modes for weak and strong signals in a retinal population D. Kastner, S. Baccus, Stanford University
II-67. Feedback inhibition in the mushroom body and gain control M. Papadopoulou, G. Turner, G. Laurent, Caltech
II-68. A likelihood framework to decode the timing of information in spiking and LFP activity A. Banerjee, H. Dean, B. Pesaran, New York University
II-69. Towards a biophysical basis of spike based inference T. Lochmann, S. Deneve, B. Gutkin, Ecole Normale Supérieure, Paris
II-70. Optimal correlation codes in populations of noisy spiking neurons G. Tkacik, J. Prentice, E. Schneidman, V. Balasubramanian, University of Pennsylvania
II-71. Phase coding of faces and objects in the superior temporal sulcus K. Hoffman, H. Turesson, A. Ghazanfar, N. Logothetis, York University
II-72. Transformation of Neural Representations in Probabilistic Population Codes L. Shi, T. Griffiths, Helen Wills Neuroscience Institute
II-73. The significance of a nonlinear transformation and a role of local neurons in an olfactory circuit R. Satoh, M. Oizumi, H. Kazama, M. Okada, the University of Tokyo
II-74. Laminar-, event-, and state-dependent population coding in the auditory cortex S. Sakata, K. Harris, Cntr for Molecul and Behav Neurosci, Rutgers
II-75. Contextual Effects in Neuronal Responses to Complex Sounds Differ between Areas AI and AAF M. Ahrens, M. Sahani, J. Linden, Gatsby Computational Neuroscience Unit, UCL
II-76. Auditory Learning Involving Complex Sounds Affects Nonlinear Integration within Cortical Responses J. Linden, I. Orduna, R. Williamson, M. Ahrens, E. Mercado III, M. Merzenich, M. Sahani, UCL Ear Institute
II-77. Spectrotemporal Modulations Underlying Speech and Timbre Perception T. Elliott, L. Hamilton, F. Theunissen, Helen Wills Neuroscience Inst., UC Berkeley
II-78. An ideal observer for passive tactile spatial perception D. Goldreich, McMaster University
II-79. What you show is what you get: sampling biases in determining biological sensory function A. Dimitrov, Montana State University
II-80. Adaptive precision pooling of model neuron activities predicts efficiency of human visual learning R. Jacobs, University of Rochester
II-81. Stability and persistence in visual cortex. P. Ulinski, University of Chicago
II-82. Orientation processing without orientation maps in the pigeon analogue to the primary visual cortex. S. W. B. Ng, A. Grabska-Barwinska, O. Guentuerkuen, D. Jancke, Ruhr-University Bochum
II-83. Timing precision in population coding of natural scenes in the early visual system G. Desbordes, J. Jin, C. Weng, N. Lesica, G. Stanley, J.-M. Alonso, Georgia Institute of Technology
II-84. Temporal dynamics of surround suppression in the corticogeniculate feedback pathway F. Briggs, W. M. Usrey, University of California, Davis
II-85. Contributions of single neurons in visual area MT to variability in smooth pursuit eye movements S. Hohl, S. Lisberger, University of California San Francisco
II-86. Extracting MAX-pooling receptive fields with natural image fragments M. Vidal-Naquet, S. Ullman, M. Tanifuji, Riken - Brain Science Institute
II-87. The effect of global context on the encoding of natural scenes. R. Haslinger, B. Lima, G. Pipa, E. Brown, S. Neuenschwander, Massachusetts General Hospital
II-88. Visual response properties of V1 neurons projecting to V2 in macaque Y. El-Shamayleh, R. Kumbhani, N. Dhruv, J. A. Movshon, New York University
II-89. [http://frontiersin.org/conferences/individual_abstract_listing.php?conferid=39&pap=1424 Homeostatic and gain control mechanisms in a developmental model of orientation map formation in V1] J. Law, J. Antolik, J. Bednar, University of Edinburgh
II-90. Simultaneous multielectrode recording and multi-photon imaging of retinal visual responses M. Menz, S. Baccus, Stanford University
II-91. Inhibition facilitates fast, robust motion detection in the visual cortex A. Sederberg, J. Liu, M. Kaschube, Princeton University
II-92. OFF direction-selective cells in the mouse retina Y. Zhang, I.-J. Kim, J. Sanes, M. Meister, Harvard University
II-93. Controlling response gain and input gain with noisy synaptic input A. Ayaz, F. Chance, UC Irvine
II-94. Context effects on visual search and rapid animal detection J. Drewes, J. Trommershäuser, K. R. Gegenfurtner, Department of Psychology, Giessen University
II-95. A motion model based on a recurrent network J. Joukes, B. Krekelberg, Rutgers University
II-96. Effects of GABAa somatic inhibition on orientation tuning and contrast sensitivity in visual cortex S. Katzner, L. Busse, A. Benucci, M. Carandini, University College London
II-97. The negative BOLD response and its behavioral correlates A. Wade, J. Rowland, Smith-Kettlewell Eye Research Institute
II-98. Functional analysis of identified interneurons in the mouse visual cortex H. Zariwala, J. Berzhanskaya, T. Zwingman, A. Jones, E. Lein, H. Zeng, K. Ahrens, Allen Institute for Brain Science
II-99. The increasing importance of secondary stimulus dimensions to V1 firing with kurtosis R. Rowekamp, T. Sharpee, University of California - San Diego
II-100. Contextual interactions in natural image contours and their possible neural implementation C. Ramachandra, B. Behabadi, R. Jain, B. Mel, University of Southern California
