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Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling
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Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling
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Journal Article
Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling
2024
Overview
Primate visual cortex exhibits key organizational principles: cortical magnification, eccentricity-dependent receptive field size and spatial frequency tuning as well as radial bias. We provide compelling evidence that these principles arise from the interplay of the non-uniform distribution of retinal ganglion cells, and a quasi-uniform convergence rate from the retina to the cortex. We show that convolutional neural networks outfitted with a retinal sampling layer, which resamples images according to retinal ganglion cell density, develop these organizational principles. Surprisingly, our results indicate that radial bias is spatial-frequency dependent and only manifests for high spatial frequencies. For low spatial frequencies, the bias shifts towards orthogonal orientations. These findings introduce a novel hypothesis about the origin of radial bias. Quasi-uniform convergence limits the range of spatial frequencies (in retinal space) that can be resolved, while retinal sampling determines the spatial frequency content throughout the retina.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
