%0 Journal Article
%T Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling.
%A da Costa D
%A Kornemann L
%A Goebel R
%A Senden M
%J Sci Rep
%V 14
%N 1
%D 2024 04 18
%M 38637554
%F 4.996
%R 10.1038/s41598-024-59376-x
%X 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.