A neurodynamical retinal network based on reaction-diffusion systems

Abstract

A dynamical model for retinal processing is presented. The model describes the output of retinal ganglion cells whose receptive field is composed of a center and a surround combining linearly. However, in comparison to the classical difference-of-Gaussian (DOG) model, center and surround are generated in two separate layers of reaction-diffusion systems, through a difference in the speed of activity-propagation between both layers. Thus, intra-layer coupling is based exclusively on next-neighbor interactions. This makes the model suitable for VLSI implementation. Furthermore, the layers are connected by equations with feedback-inhibition to form ON-center/OFF-surround and OFF-center/OFF-surround receptive fields. The model's output in the early dynamics corresponds to high-resolution contrast information, whereas the output at later times can be considered as correlated with local brightness and darkness, respectively. To examine this in more detail, simulations with the Hermann/Hering-grid and grating induction were carried out.