Spatio-Chromatic Information available from different Neural Layers

J. Malo, Journal of Mathematical Neuroscience 2020

The image representations along the retina-cortex pathway are analyzed in terms of their ability to capture information about the visual scenes. The considered series of representations includes: (1) the LMS retinal images, (2) their von-Kries adapted version, (3) the opponent images at LGN, (4) their nonlinear version after Weber-like saturation, (5) the cortical local-frequency representation filtered by achromatic and chromatic CSFs, and (6) the cortical representation after divisive normalization.
Assuming a single-step transform from the retinal input to each of these representations, and sensors of the same Signal-to-Noise quality in each representation, our estimations of transmitted information show that: (a) progressively deeper representations are better in terms of the amount of captured information, (b) the transmitted information up to the cortical representation follows the probability of natural scenes over the chromatic and achromatic dimensions of the stimulus space, (c) the contribution of spatial transforms to capture visual information is substantially greater (67%) than the contribution of chromatic transforms (33%), and (d) nonlinearities of the responses contribute substantially to the transmitted information (about 28%) but less than the linear transforms (72%).
The parameters of the model have psychophysical origin and they were not statistically optimized in any way. The information estimates were computed using our Gaussianization transform (Laparra et al. IEEE TNN 11) over our database of colorimetrically-calibrated images (Laparra et al. Neur.Comp. 12), but equivalent results are obtained using other estimates (e.g. offset corrected Kozachenko-Leonenko [Marin et al. IEEE PAMI 13]) or other databases (e.g. Foster et al [Vis.Res.15,16]).

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Citation and References


J. Malo.
Spatio-Chromatic Information available from different Neural Layers via Gaussianization
J. Mathematical Neuroscience (2020) mat

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