The joint probability density function for linear optic flow components

Artificial neural models suggest that the probability density function (PDF) of available inputs is crucial in determining the distribution of responses shown by a group of pattern-matching cells. This paper therefore describes a Monte-Carlo study of the PDF for linear optic flow components produced by ego-motion in a simulated planar environment. The recent search for deformation-selective cells in the medial superior temporal (MST) area of the cerebral cortex is used to illustrate the biological significance of the optic flow PDF. The simulation results are consistent with the neurophysiological finding that MST cells exhibit a continuum of responses to translation, rotation and divergence. In addition, there are strong negative correlations between deformation and other first-order flow components. The deformation components contain information necessary for recovering shape from flow. Consequently, if cells responsible for shape analysis are present in the MST area they should respond best to combinations of deformation with other flow components, rather than to the pure stimuli used in previous neurophysiological studies