Independent component filters of natural images compared with simple cells in primary visual cortex

Properties of the receptive fields of simple cells in macaque cortex were compared with properties of independent component filters generated by independent component analysis (ICA) on a large set of natural images. Histograms of spatial frequency bandwidth, orientation tuning bandwidth, aspect ratio and length of the receptive fields match well. This indicates that simple cells are well tuned to the expected statistics of natural stimuli. There is no match, however, in calculated and measured distributions for the peak of the spatial frequency response: the filters produced by ICA do not vary their spatial scale as much as simple cells do, but are fixed to scales close to the finest ones allowed by the sampling lattice. Possible ways to resolve this discrepancy are discussed.

[1]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

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

[3]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[4]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[5]  S. Laughlin.  A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.

[6]  S. Laughlin,et al.  Predictive coding: a fresh view of inhibition in the retina , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[7]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

[8]  D. Field,et al.  The structure and symmetry of simple-cell receptive-field profiles in the cat’s visual cortex , 1986, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[9]  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.

[10]  A. Parker,et al.  Two-dimensional spatial structure of receptive fields in monkey striate cortex. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[11]  P. Lennie,et al.  Contrast adaptation in striate cortex of macaque , 1989, Vision Research.

[12]  J. V. van Hateren,et al.  Real and optimal neural images in early vision , 1992, Nature.

[13]  Ralph Linsker,et al.  Deriving Receptive Fields Using an Optimal Encoding Criterion , 1992, NIPS.

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

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

[16]  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.

[17]  D. Ruderman.  The statistics of natural images , 1994 .

[18]  Horace Barlow,et al.  What is the computational goal of the neocortex , 1994 .

[19]  Zhaoping Li,et al.  Towards a theory of striate cortex , 1994 .

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

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

[22]  Trichur Raman Vidyasagar,et al.  A linear model fails to predict orientation selectivity of cells in the cat visual cortex. , 1996, The Journal of physiology.

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

[24]  Erkki Oja,et al.  Image Feature Extraction Using Independent Component Analysis , 1996 .

[25]  Victor A. F. Lamme,et al.  Contextual Modulation in Primary Visual Cortex , 1996, The Journal of Neuroscience.

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

[27]  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.

[28]  Erkki Oja,et al.  One-unit Learning Rules for Independent Component Analysis , 1996, NIPS.

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

[30]  V. Hateren,et al.  Processing of natural time series of intensities by the visual system of the blowfly , 1997, Vision Research.

[31]  Bruno A. Olshausen,et al.  Inferring Sparse, Overcomplete Image Codes Using an Efficient Coding Framework , 1998, NIPS.

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

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

[34]  Jarmo Hurri,et al.  Independent Component Analysis of Image Data , 1997 .

[35]  RussLL L. Ds Vnlos,et al.  SPATIAL FREQUENCY SELECTIVITY OF CELLS IN MACAQUE VISUAL CORTEX , 2022 .