Distinctive Features of Asymmetric Neural Networks with Gabor Filters

Abstract

To make clear the mechanism of the visual motion detection is important in the visual system, which is useful to robotic systems. The prominent features are the nonlinear characteristics as the squaring and rectification functions, which are observed in the retinal and visual cortex networks. Conventional models for motion processing, are to use symmetric quadrature functions with Gabor filters. This paper proposes a new motion processing model of the asymmetric networks. To analyze the behavior of the asymmetric nonlinear network, white noise analysis and Wiener kernels are applied. It is shown that the biological asymmetric network with nonlinearities is effective for generating the directional movement from the network computations. Further, responses to complex stimulus and the frequency characteristics are computed in the asymmetric networks, which are not derived for the conventional energy model.