A model for the economical encoding of the visual image in cerebral cortex

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Abstract
We propose a model for the first stage of the cortical transformation of the visual image based on the principle that the cortex encodes the information with the minimum number of channels mathematically needed. We restrict our model to be consistent with the data on size adaptation, the known relationships of acuity and the inverse of magnification factor with eccentricity, and the electrophysiological findings on the physiological uniformity of the striate cortex. Assuming that each hypercolumn analyzes a limited spatial domain, we apply the sampling theorem to show that only 16 channels, composed of 4 sizes, are needed for one dimension. The extension to 2 dimensions leads to a possible scheme for the number, spacing, and orientational disposition of the elements, together with predictions about the number of inputs from the eyes and the total number of hypercolumns. Since all these predictions are consistent with physical and neural estimates, we conclude that the cortex may analyze the image along the lines we suggest.