ConvNets Develop Organizational Principles of the Visual Cortex when using Ganglion Cell-Based Sampling

Abstract

The distribution of retinal ganglion cells in primate visual systems portrays a densely distributed central region, with an incrementally decreasing cell density as the angle of visual eccentricity increases. This results in a non-uniform sampling of the retinal image that resembles a wheelbarrow distortion. We propose that this sampling gives rise to several organizational properties of the primate visual system, including cortical magnification, linear relationship between eccentricity and receptive field sizes, eccentricity-dependent drop-off in spatial-frequency preference, and radial bias. We test this hypothesis by training a convolutional neural network to classify the orientation of sine gratings and Gabor stimuli, resampled according to retinal ganglion cell distributions. Our simulations show that introducing this sampling step gives rise to the aforementioned organizational principles in convolutional layers while only minimally affecting their classification performance. This lends credence to the notion that the retinal ganglion cell distribution is an important factor for the emergence of these organizational principles in visual systems.