Recurrent network simulations of two types of non-concentric retinal ganglion cells

Recurrent network models are widely used to characterize the behaviors of neurons in the visual cortex. However, they are seldom used to simulate neurons in the retina. In this study, two slightly different recurrent network models are introduced to describe two types of non-concentric ganglion cells, i.e., the impressed-by-contrast cell and the suppressed-by-contrast cell in the cat retina. By simulations, it is found that the additive recurrent network is able to describe qualitatively the behavior of the impressed-by-contrast cell, while the other additive recurrent network with saturation rectification is able to describe qualitatively the behavior of the suppressed-by-contrast cell.