Skeleton model for the neurodynamics of visual action representations

Abstract

The visual recognition of body motion in the primate brain requires the temporal integration of information over complex patterns, potentially exploiting recurrent neural networks consisting of shape- and optic-flow-selective neurons. The paper presents a mathematically simple neurodynamical model that approximates the mean-field dynamics of such networks. It is based on a two-dimensional neural field with appropriate lateral interaction kernel and an adaptation process for the individual neurons. The model accounts for a number of, so far not modeled, observations in the recognition of body motion, including perceptual multi-stability and the weakness of repetition suppression, as observed in single-cell recordings for the repeated presentation of action stimuli. In addition, the model predicts novel effects in the perceptual organization of action stimuli. © 2014 Springer International Publishing Switzerland.