Asymmetric Neural Networks with Gabor Filters

To make clear the mechanism of the visual movement is important in he visual system. The prominent feature is the nonlinear characteristics as the squaring and rectification functions, which are observed in the retinal and visual cortex networks. Many popular models for motion processing in cortex, is the use of symmetric quadrature functions with Gabor filters. This paper proposes a new motion sensing processing model in the asymmetric networks. To make clear 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 and superior for generating the directional movement from the network computations. The directive equations for the stimulus were derived by applying the white noise analysis and kernels to characterize stimulus changes. The results are applicable to the V1 and MT model of the neural networks in the cortex.