Comparison of Asymmetric and Symmetric Neural Networks with Gabor Filters

Visual motion information is useful for many complex tasks in the biological and robotic systems. Models for motion processing in the biological systems have been studied to use conventional symmetric quadrature functions with Gabor filters. This paper proposes a model of the another bio-inspired asymmetric neural networks. The prominent features are the nonlinear characteristics as the squaring and rectification functions, which are observed in the retinal and visual cortex networks. In this paper, the asymmetric network with Gabor filters is compared with that of the conventional symmetric networks. It is shown that the biological asymmetric network with nonlinearities is effective for detecting the inputted phase information and directional movements from the network computations. The responses to the frequency characteristics and to the complex motion stimulus are computed in the asymmetric networks, which are not derived for the conventional energy model.