The use of CNN models in the subcortical visual pathway

Abstract

The equivalent notions of neuroanatomy and the cellular neural network (CNN) model are discussed with a view toward studying the visual system. Various mainly subcortical phenomena are studied and simple effects like directional sensitivity and length tuning are modeled. A more accurate retina model has been developed, taking into account some effects of amacrine cells. It is shown that the standard errors occurring in simple models of retinal illusions can be eliminated by using the more accurate models including delays. Lateral geniculate nucleus (LGN) effects with and without cortical feedback are modeled as well; their CNN models are simple. Simple texture detection effects and motion illusions are explained by neuromorphic CNN models. The goal is to translate known effects into CNN models and to provide a framework for further studies.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>