Application of Asymmetric Networks to Motion Detection and Generating Independent Subspaces

Abstract

The prominent feature in the visual system is the nonlinear characteristics as the squaring and rectification functions, which are observed in the retinal and visual cortex networks. Conventional model for motion processing in cortex, uses a symmetric quadratic functions with Gabor filters. This paper proposes a new motion processing model in the asymmetric networks. First, the asymmetric network is analyzed using white noise analysis and Wiener kernels. It is shown that the asymmetric network with nonlinearities is effective and general for generating the directional movement compared with the conventional quadratic model. Second, independence maximization of data is an important issue in computational neural networks. To make clear the asymmetric network with Gabor functions, orthogonality is computed, which shows independence characteristics of the asymmetric structured network without conventional maximizing independence in the quadratic model. The orthogonal analyses for the independence of the asymmetric neural networks are applied to the V1 and MT neural networks model to generate independent subspaces.