Scientific Reports (Apr 2024)

Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling

  • Danny da Costa,
  • Lukas Kornemann,
  • Rainer Goebel,
  • Mario Senden

DOI
https://doi.org/10.1038/s41598-024-59376-x
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 14

Abstract

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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.

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