Vector generation and operations in neural networks computations

Abstract

To make clear the mechanism of the visual movement is important in the visual system. The problem is how to perceive vectors of the optic flow in the network. First, the biological asymmetric network with nonlinearities is analyzed for generating the vector from the point of the network computations. The results are applicable to the V1 and MT model of the neural networks in the cortex. The stimulus with a mixture distribution is applied to evaluate their network processing ability for the movement direction and its velocity, which generate the vector. Second, it is shown that the vector is emphasized in the MT than the V1. The characterized equation is derived in the network computations, which evaluates the vector properties of processing ability of the network. The movement velocity is derived, which is represented in Wiener kernels. The operations of vectors are shown in the divisive normalization network, which will create curl or divergence vectors in the higher neural network as MST area. © 2013 Springer-Verlag Berlin Heidelberg.