A Neurodynamical Retinal Network Based on Reaction-Diffusion Systems

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 OFFcenter/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.