Layered Network Computations by Parallel Nonlinear Processing

Abstract

Among visual processings in the visual networks, movement detections are carried out in the visual cortex. The visual cortex for the movement detection, consist of two layered networks, called the primary visual cortex (V1), followed by the middle temporal area (MT). In the biological visual neural networks, a characteristic feature is nonlinear functions, which will play important roles in the visual systems. In this paper, V1 and MT model networks, are decomposed into sub-asymmetrical networks. By the optimization of the asymmetric networks, movement detection equations are derived. Then, it was clarified that asymmetric networks with the even-odd nonlinearity combined , are fundamental in the movement detection. These facts are applied to two layered V1 and MT networks, in which it was clarified that the second layer MT has an efficient ability to detect the movement.