What Does the Retina Know about Natural Scenes?

By examining the experimental data on the statistical properties of natural scenes together with (retinal) contrast sensitivity data, we arrive at a first principle, theoretical hypothesis for the purpose of retinal processing and its relationship to an animal's environment. We argue that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex, as suggested by Barlow. The extent of this whitening of the input is limited, however, by the need to suppress input noise. Our explicit theoretical solutions for the retinal filters also show a simple dependence on mean stimulus luminance: they predict an approximate Weber law at low spatial frequencies and a De Vries-Rose law at high frequencies. Assuming that the dominant source of noise is quantum, we generate a family of contrast sensitivity curves as a function of mean luminance. This family is compared to psychophysical data.

[1]  M. C. GOODALL,et al.  Performance of a Stochastic Net , 1960, Nature.

[2]  M. A. Bouman,et al.  Spatial Modulation Transfer in the Human Eye , 1967 .

[3]  D. H. Kelly Adaptation effects on spatio-temporal sine-wave thresholds. , 1972, Vision research.

[4]  D. Hubel,et al.  Sequence regularity and geometry of orientation columns in the monkey striate cortex , 1974, The Journal of comparative neurology.

[5]  R. L. de Valois,et al.  Psychophysical studies of monkey vision. 3. Spatial luminance contrast sensitivity tests of macaque and human observers. , 1974, Vision Research.

[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]  P. Lennie,et al.  The influence of temporal frequency and adaptation level on receptive field organization of retinal ganglion cells in cat , 1982, The Journal of physiology.

[8]  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.

[9]  Ralph Linsker,et al.  An Application of the Principle of Maximum Information Preservation to Linear Systems , 1988, NIPS.

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

[11]  Joseph J. Atick,et al.  Towards a Theory of Early Visual Processing , 1990, Neural Computation.

[12]  Zhaoping Li,et al.  Understanding Retinal Color Coding from First Principles , 1992, Neural Computation.

[13]  Kenneth D. Miller Introduction: The use of models in the neurosciences , 1992 .

[14]  Joseph J. Atick,et al.  Convergent Algorithm for Sensory Receptive Field Development , 1993, Neural Computation.

[15]  A. Norman Redlich,et al.  Redundancy Reduction as a Strategy for Unsupervised Learning , 1993, Neural Computation.

[16]  A. Norman Redlich,et al.  Supervised Factorial Learning , 1993, Neural Computation.