TemporalPrecisionintheVisualPathwaythroughthe InterplayofExcitationandStimulus-DrivenSuppression

[1]  Eero P. Simoncelli,et al.  Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.

[2]  W. Gerstner,et al.  Chapter 12 A framework for spiking neuron models: The spike response model , 2001 .

[3]  William Bialek,et al.  Synergy in a Neural Code , 2000, Neural Computation.

[4]  Michael J. Berry,et al.  The Neural Code of the Retina , 1999, Neuron.

[5]  Michael J. Berry,et al.  Selectivity for multiple stimulus features in retinal ganglion cells. , 2006, Journal of neurophysiology.

[6]  E J Chichilnisky,et al.  A simple white noise analysis of neuronal light responses , 2001, Network.

[7]  D. Contreras,et al.  Dynamics of excitation and inhibition underlying stimulus selectivity in rat somatosensory cortex , 2005, Nature Neuroscience.

[8]  Christopher L Passaglia,et al.  Information transmission rates of cat retinal ganglion cells. , 2004, Journal of neurophysiology.

[9]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[10]  R. Reid,et al.  Temporal Coding of Visual Information in the Thalamus , 2000, The Journal of Neuroscience.

[11]  M. Carandini,et al.  Functional Mechanisms Shaping Lateral Geniculate Responses to Artificial and Natural Stimuli , 2008, Neuron.

[12]  Guangying K. Wu,et al.  Lateral Sharpening of Cortical Frequency Tuning by Approximately Balanced Inhibition , 2008, Neuron.

[13]  Michael J. Berry,et al.  Anticipation of moving stimuli by the retina , 1999, Nature.

[14]  N. C. Singh,et al.  Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli , 2001 .

[15]  W. Martin Usrey,et al.  Spike Timing and Information Transmission at Retinogeniculate Synapses , 2010, The Journal of Neuroscience.

[16]  T. Albright,et al.  Efficient Discrimination of Temporal Patterns by Motion-Sensitive Neurons in Primate Visual Cortex , 1998, Neuron.

[17]  B. Cleland,et al.  Organization of visual inputs to interneurons of lateral geniculate nucleus of the cat. , 1977, Journal of neurophysiology.

[18]  Robert Shapley,et al.  Linear and nonlinear systems analysis of the visual system: Why does it seem so linear? A review dedicated to the memory of Henk Spekreijse , 2009, Vision Research.

[19]  Lawrence C. Sincich,et al.  Preserving Information in Neural Transmission , 2009, The Journal of Neuroscience.

[20]  Matteo Carandini,et al.  Thalamic filtering of retinal spike trains by postsynaptic summation. , 2007, Journal of vision.

[21]  J D Victor,et al.  How the contrast gain control modifies the frequency responses of cat retinal ganglion cells. , 1981, The Journal of physiology.

[22]  J. B. Demb,et al.  Disinhibition Combines with Excitation to Extend the Operating Range of the OFF Visual Pathway in Daylight , 2008, The Journal of Neuroscience.

[23]  Nicholas J. Priebe,et al.  Mechanisms underlying cross-orientation suppression in cat visual cortex , 2006, Nature Neuroscience.

[24]  John P. Miller,et al.  Temporal encoding in nervous systems: A rigorous definition , 1995, Journal of Computational Neuroscience.

[25]  Eero P. Simoncelli,et al.  Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.

[26]  Jessica A. Cardin,et al.  Stimulus Feature Selectivity in Excitatory and Inhibitory Neurons in Primary Visual Cortex , 2007, The Journal of Neuroscience.

[27]  Xin Wang,et al.  Recoding of Sensory Information across the Retinothalamic Synapse , 2010, The Journal of Neuroscience.

[28]  Matteo Carandini,et al.  Somatosensory Integration Controlled by Dynamic Thalamocortical Feed-Forward Inhibition , 2005, Neuron.

[29]  E. Chichilnisky,et al.  Adaptation to Temporal Contrast in Primate and Salamander Retina , 2001, The Journal of Neuroscience.

[30]  Alexander Borst,et al.  Information theory and neural coding , 1999, Nature Neuroscience.

[31]  Pamela Reinagel,et al.  Contrast adaptation in a nonadapting LGN model. , 2007, Journal of neurophysiology.

[32]  L. Paninski,et al.  Inferring input nonlinearities in neural encoding models , 2008, Network.

[33]  Konrad Paul Kording,et al.  Processing of complex stimuli and natural scenes in the visual cortex , 2004, Current Opinion in Neurobiology.

[34]  R. Shapley,et al.  The receptive field organization of X-cells in the cat: Spatiotemporal coupling and asymmetry , 1984, Vision Research.

[35]  David R. Brillinger,et al.  Nerve Cell Spike Train Data Analysis: A Progression of Technique , 1992 .

[36]  Garrett B Stanley,et al.  Decoupling functional mechanisms of adaptive encoding , 2006, Network.

[37]  L. Paninski Maximum likelihood estimation of cascade point-process neural encoding models , 2004, Network.

[38]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[39]  Romesh D Kumbhani,et al.  Precision, reliability, and information-theoretic analysis of visual thalamocortical neurons. , 2007, Journal of neurophysiology.

[40]  T. Sharpee,et al.  Estimating linear–nonlinear models using Rényi divergences , 2009, Network.

[41]  J. B. Demb,et al.  Contrast Adaptation in Subthreshold and Spiking Responses of Mammalian Y-Type Retinal Ganglion Cells , 2005, The Journal of Neuroscience.

[42]  Fred Rieke,et al.  Network Variability Limits Stimulus-Evoked Spike Timing Precision in Retinal Ganglion Cells , 2006, Neuron.

[43]  Wade G. Regehr,et al.  Timing and Specificity of Feed-Forward Inhibition within the LGN , 2005, Neuron.

[44]  Garrett B Stanley,et al.  Timing Precision in Population Coding of Natural Scenes in the Early Visual System , 2008, PLoS biology.

[45]  Uri T Eden,et al.  A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.

[46]  Brian Lau,et al.  Computational subunits of visual cortical neurons revealed by artificial neural networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[47]  Liam Paninski,et al.  Convergence properties of three spike-triggered analysis techniques , 2003, NIPS.

[48]  Youping Xiao,et al.  A simple model of retina-LGN transmission , 2008, Journal of Computational Neuroscience.

[49]  Ryan J. Prenger,et al.  Nonlinear V1 responses to natural scenes revealed by neural network analysis , 2004, Neural Networks.

[50]  Elena A Allen,et al.  Dynamic Spatial Processing Originates in Early Visual Pathways , 2006, The Journal of Neuroscience.

[51]  M J Korenberg,et al.  Dissection of the neuron network in the catfish inner retina. III. Interpretation of spike kernels. , 1989, Journal of neurophysiology.

[52]  Michael Okun,et al.  Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities , 2008, Nature Neuroscience.

[53]  Chun-I Yeh,et al.  Receptive field size and response latency are correlated within the cat visual thalamus. , 2005, Journal of neurophysiology.

[54]  Kamiar Rahnama Rad,et al.  Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods , 2010, Network.

[55]  William Bialek,et al.  Real-time performance of a movement-sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[56]  Nicole C. Rust,et al.  Do We Know What the Early Visual System Does? , 2005, The Journal of Neuroscience.

[57]  W. Martin Usrey,et al.  Origin and Dynamics of Extraclassical Suppression in the Lateral Geniculate Nucleus of the Macaque Monkey , 2008, Neuron.

[58]  Wei Wu,et al.  A new look at state-space models for neural data , 2010, Journal of Computational Neuroscience.

[59]  Chun-I Yeh,et al.  Temporal precision in the neural code and the timescales of natural vision , 2007, Nature.

[60]  Eero P. Simoncelli,et al.  To appear in: The New Cognitive Neurosciences, 3rd edition Editor: M. Gazzaniga. MIT Press, 2004. Characterization of Neural Responses with Stochastic Stimuli , 2022 .

[61]  Liam Paninski,et al.  Statistical models for neural encoding, decoding, and optimal stimulus design. , 2007, Progress in brain research.

[62]  Eero P. Simoncelli,et al.  Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis. , 2006, Journal of vision.

[63]  Xin Wang,et al.  Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing , 2010, Nature Neuroscience.

[64]  Robert C. Liu,et al.  Variability and information in a neural code of the cat lateral geniculate nucleus. , 2001, Journal of neurophysiology.

[65]  William Bialek,et al.  Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions , 2002, Neural Computation.

[66]  R. Reid,et al.  Predicting Every Spike A Model for the Responses of Visual Neurons , 2001, Neuron.

[67]  Garrett B Stanley,et al.  The episodic nature of spike trains in the early visual pathway. , 2010, Journal of neurophysiology.

[68]  J. Gallant,et al.  Complete functional characterization of sensory neurons by system identification. , 2006, Annual review of neuroscience.

[69]  R. Shapley,et al.  The effect of contrast on the transfer properties of cat retinal ganglion cells. , 1978, The Journal of physiology.