A neural model of image velocity encoding

A self-organizing neural network is presented which uses, as input, Fourier information about the image of moving objects to represent the magnitude and orientation of their velocity vectors topologically over the neural layer. The input space consists of the outputs of spatio-temporal frequency and orientation tuned filters. The motion that is encoded is that in the plane perpendicular to the line of sight. The neural model of velocity encoding incorporates biologically plausible elements into a computational theory based on Fourier analysis of the image. At the systems level, the model may be represented as a two-stage process. The first stage is composed of input cell filters which extract information in the Fourier domain, and the second stage is the velocity encoding net itself.<>