INDEPENDENT COMPONENT ANALYSIS OF NATURAL IMAGE SEQUENCES YIELDS SPATIOTEMPORAL FILTERS SIMILAR TO SIMPLE CELLS IN PRIMARY VISUAL CORTEX

Simple cells in primary visual cortex process incoming visual information with receptive fields localized in space and time, bandpass in spatial and temporal frequency, tuned in orientation, and commonly selective for the direction of movement. It is shown that performing independent component analysis on video sequences of natural scenes produces results with qualitatively similar spatiotemporal properties. Whereas the independent components of video resemble moving edges or bars, the independent component filters, i.e. the analogues of receptive fields, resemble moving sinusoids windowed by steady gaussian envelopes. Contrary to earlier ICA results on static images, which gave only filters at the finest possible spatial scale, the spatiotemporal analysis yields filters at a range of spatial and temporal scales. Filters centered at low spatial frequencies are generally tuned to faster movement than those at high spatial frequencies.

[1]  H B Barlow,et al.  Single units and sensation: a neuron doctrine for perceptual psychology? , 1972, Perception.

[2]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[3]  H. B. Barlow,et al.  What does the eye see best? , 1983, Nature.

[4]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[5]  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.

[6]  C L Baker,et al.  Spatial- and temporal-frequency selectivity as a basis for velocity preference in cat striate cortex neurons , 1990, Visual Neuroscience.

[7]  K. D. De Valois,et al.  Vernier acuity with stationary moving Gabors. , 1991, Vision research.

[8]  Bernhard Wegmann,et al.  The importance of intrinsically two-dimensional image features in biological vision and picture coding , 1993 .

[9]  I. Ohzawa,et al.  Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. , 1993, Journal of neurophysiology.

[10]  J. H. Van Hateren,et al.  Spatiotemporal contrast sensitivity of early vision , 1993, Vision Research.

[11]  I. Ohzawa,et al.  Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. I. General characteristics and postnatal development. , 1993, Journal of neurophysiology.

[12]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[13]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[14]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[15]  A. Watson,et al.  The optimal motion stimulus , 1995, Vision Research.

[16]  J. Atick,et al.  Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus , 1995 .

[17]  Terrence J. Sejnowski,et al.  Edges are the Independent Components of Natural Scenes , 1996, NIPS.

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

[19]  Daniel L. Ruderman,et al.  Origins of scaling in natural images , 1996, Vision Research.

[20]  Dawei W. Dong.  Spatiotemporal Coupling and Scaling of Natural Images and Human Visual Sensitivities , 1996, NIPS.

[21]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[22]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[23]  A. Hyvarinen.  A family of fixed-point algorithms for independent component analysis , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[24]  George Francis Harpur,et al.  Low Entropy Coding with Unsupervised Neural Networks , 1997 .

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

[26]  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.

[27]  Eero P. Simoncelli,et al.  A model of neuronal responses in visual area MT , 1998, Vision Research.

[28]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.