Natural signal statistics and sensory gain control

We describe a form of nonlinear decomposition that is well-suited for efficient encoding of natural signals. Signals are initially decomposed using a bank of linear filters. Each filter response is then rectified and divided by a weighted sum of rectified responses of neighboring filters. We show that this decomposition, with parameters optimized for the statistics of a generic ensemble of natural images or sounds, provides a good characterization of the nonlinear response properties of typical neurons in primary visual cortex or auditory nerve, respectively. These results suggest that nonlinear response properties of sensory neurons are not an accident of biological implementation, but have an important functional role.

[1]  F. Attneave.  Some informational aspects of visual perception. , 1954, Psychological review.

[2]  J. E. Rose,et al.  Some effects of stimulus intensity on response of auditory nerve fibers in the squirrel monkey. , 1971, Journal of neurophysiology.

[3]  L. Maffei,et al.  The unresponsive regions of visual cortical receptive fields , 1976, Vision Research.

[4]  C. D. Geisler,et al.  Two-tone suppression in auditory nerve of the cat: rate-intensity and temporal analyses. , 1978, The Journal of the Acoustical Society of America.

[5]  C. Enroth-Cugell,et al.  Chapter 9 Visual adaptation and retinal gain controls , 1984 .

[6]  J. Victor.  The dynamics of the cat retinal X cell centre. , 1987, The Journal of physiology.

[7]  I. Ohzawa,et al.  The effects of contrast on visual orientation and spatial frequency discrimination: a comparison of single cells and behavior. , 1987, Journal of neurophysiology.

[8]  A. B. Bonds.  Role of Inhibition in the Specification of Orientation Selectivity of Cells in the Cat Striate Cortex , 1989, Visual Neuroscience.

[9]  Peter Földiák,et al.  Adaptation and decorrelation in the cortex , 1989 .

[10]  Richard Durbin,et al.  The computing neuron , 1989 .

[11]  T. Wiesel,et al.  The influence of contextual stimuli on the orientation selectivity of cells in primary visual cortex of the cat , 1990, Vision Research.

[12]  Richard F. Lyon,et al.  Automatic Gain Control in Cochlear Mechanics , 1990 .

[13]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[14]  C. Daniel Geisler,et al.  The Mechanics and Biophysics of Hearing , 1990 .

[15]  D. V. van Essen,et al.  Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. , 1992, Journal of neurophysiology.

[16]  D. G. Albrecht,et al.  Cortical neurons: Isolation of contrast gain control , 1992, Vision Research.

[17]  Edward H. Adelson,et al.  Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.

[18]  I. Ohzawa,et al.  Organization of suppression in receptive fields of neurons in cat visual cortex. , 1992, Journal of neurophysiology.

[19]  D. Heeger.  Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[20]  David J. Field,et al.  Contour integration by the human visual system: Evidence for a local “association field” , 1993, Vision Research.

[21]  William Bialek,et al.  Statistics of Natural Images: Scaling in the Woods , 1993, NIPS.

[22]  J. M. Foley,et al.  Human luminance pattern-vision mechanisms: masking experiments require a new model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  Kuansan Wang,et al.  Self-normalization and noise-robustness in early auditory representations , 1994, IEEE Trans. Speech Audio Process..

[24]  W. Bialek,et al.  Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[25]  H. Jones,et al.  Visual cortical mechanisms detecting focal orientation discontinuities , 1995, Nature.

[26]  B. Delgutte.  Physiological Models for Basic Auditory Percepts , 1996 .

[27]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[28]  R. Fay.  Auditory Computation , 1996, Springer Handbook of Auditory Research.

[29]  T J Sejnowski,et al.  Learning the higher-order structure of a natural sound. , 1996, Network.

[30]  L. Abbott,et al.  Synaptic Depression and Cortical Gain Control , 1997, Science.

[31]  J. Movshon,et al.  Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex , 1997, The Journal of Neuroscience.

[32]  J. B. Levitt,et al.  Contrast dependence of contextual effects in primate visual cortex , 1997, nature.

[33]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[34]  Malcolm Slaney,et al.  An Efficient Implementation of the Patterson-Holdsworth Auditory Filter Bank , 1997 .

[35]  R. Reid,et al.  Synaptic Integration in Striate Cortical Simple Cells , 1998, The Journal of Neuroscience.

[36]  J. V. van Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[37]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[38]  C. D. Geisler,et al.  From Sound to Synapse: Physiology of the Mammalian Ear , 1998 .

[39]  Y. Frégnac,et al.  Visual input evokes transient and strong shunting inhibition in visual cortical neurons , 1998, Nature.

[40]  R. Shapley,et al.  Contrast's effect on spatial summation by macaque V1 neurons , 1999, Nature Neuroscience.

[41]  G Westheimer,et al.  Dynamics of spatial summation in primary visual cortex of alert monkeys. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Hong-Bo Zhao,et al.  Auditory collusion and a coupled couple of outer hair cells , 1999, Nature.

[43]  N. Graham,et al.  Normalization: contrast-gain control in simple (Fourier) and complex (non-Fourier) pathways of pattern vision , 2000, Vision Research.

[44]  M. Carandini,et al.  Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex. , 2000, Journal of neurophysiology.

[45]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[46]  C. Gilbert,et al.  On a common circle: natural scenes and Gestalt rules. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[47]  Eero P. Simoncelli,et al.  Random Cascades on Wavelet Trees and Their Use in Analyzing and Modeling Natural Images , 2001 .

[48]  Jeffrey S. Perry,et al.  Edge co-occurrence in natural images predicts contour grouping performance , 2001, Vision Research.

[49]  P. Latham,et al.  Retinal ganglion cells act largely as independent encoders , 2001, Nature.

[50]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[51]  Eero P. Simoncelli,et al.  Natural image statistics and divisive normalization: Modeling nonlinearity and adaptation in cortical neurons , 2002 .

[52]  Rajesh P. N. Rao,et al.  Probabilistic Models of the Brain: Perception and Neural Function , 2002 .