Abstract

The prominent feature 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 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 characteristics of the asymmetric network with Gabor functions, orthogonality is computed, which shows independent characteristics of the asymmetric network without maximizing optimization of independence in the quadratic model. The orthogonal analyses for the independence of the asymmetric networks are applied to the V1 and MT neural networks to generate independent subspaces by using selective Gabor functions.

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