Sparse coding and decorrelation in primary visual cortex during natural vision.

Theoretical studies suggest that primary visual cortex (area V1) uses a sparse code to efficiently represent natural scenes. This issue was investigated by recording from V1 neurons in awake behaving macaques during both free viewing of natural scenes and conditions simulating natural vision. Stimulation of the nonclassical receptive field increases the selectivity and sparseness of individual V1 neurons, increases the sparseness of the population response distribution, and strongly decorrelates the responses of neuron pairs. These effects are due to both excitatory and suppressive modulation of the classical receptive field by the nonclassical receptive field and do not depend critically on the spatiotemporal structure of the stimuli. During natural vision, the classical and nonclassical receptive fields function together to form a sparse representation of the visual world. This sparse code may be computationally efficient for both early vision and higher visual processing.

[1]  Amtliches Mitteilungsblatt,et al.  August , 1890, The Hospital.

[2]  R. Wurtz,et al.  Vision during saccadic eye movements. I. Visual interactions in striate cortex. , 1980, Journal of neurophysiology.

[3]  J. Allman,et al.  Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons. , 1985, Annual review of neuroscience.

[4]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[5]  J.G. Daugman,et al.  Entropy reduction and decorrelation in visual coding by oriented neural receptive fields , 1989, IEEE Transactions on Biomedical Engineering.

[6]  P. M. D. Lorenzo,et al.  Across unit patterns in the neural response to taste: vector space analysis. , 1989 .

[7]  Thomas G. Dietterich,et al.  In Advances in Neural Information Processing Systems 12 , 1991, NIPS 1991.

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

[9]  C. Gilbert Horizontal integration and cortical dynamics , 1992, Neuron.

[10]  M. Young,et al.  Sparse population coding of faces in the inferotemporal cortex. , 1992, Science.

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

[12]  B. C. Motter Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. , 1993, Journal of neurophysiology.

[13]  D. Snodderly,et al.  Organization of striate cortex of alert, trained monkeys (Macaca fascicularis): ongoing activity, stimulus selectivity, and widths of receptive field activating regions. , 1995, Journal of neurophysiology.

[14]  E T Rolls,et al.  Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. , 1995, Journal of neurophysiology.

[15]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

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

[17]  C. Gilbert,et al.  Spatial integration and cortical dynamics. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

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

[19]  William B. Levy,et al.  Energy Efficient Neural Codes , 1996, Neural Computation.

[20]  R C Reid,et al.  Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory , 1996, The Journal of Neuroscience.

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

[22]  D. V. van Essen,et al.  Spatial Attention Effects in Macaque Area V4 , 1997, The Journal of Neuroscience.

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

[24]  Rob R. de Ruyter van Steveninck,et al.  The metabolic cost of neural information , 1998, Nature Neuroscience.

[25]  D C Van Essen,et al.  Neural activity in areas V1, V2 and V4 during free viewing of natural scenes compared to controlled viewing , 1998, Neuroreport.

[26]  Rajeev Sharma,et al.  Advances in Neural Information Processing Systems 11 , 1999 .

[27]  G. McFadden,et al.  Mitochondrial FtsZ in a chromophyte alga. , 2000, Science.