{Reference Type}: Journal Article
{Title}: Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling.
{Author}: da Costa D;Kornemann L;Goebel R;Senden M;
{Journal}: Sci Rep
{Volume}: 14
{Issue}: 1
{Year}: 2024 04 18
{Factor}: 4.996
{DOI}: 10.1038/s41598-024-59376-x
{Abstract}: 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.