Biologically motivated model for encoding multiple object motions in primate visual cortex using sequences of natural images

We examine the possibility that the spatiotemporal receptive field properties of visual cortical neurons can be understood in terms of a statistically efficient strategy for encoding natural time-varying images. It is believed that the sense of object motion and velocity are also related to these fields, as objects in natural scenes are represented by a sparse set of statistically independent components, such as edges. Currently, computational models of receptive fields consider only spatial components, and thus cannot account for time-varying sensory stimuli. In this paper, a model based on independent components analysis and cellular neural networks is proposed. The paper describes an artificial neural network that attempts to accurately reconstruct its spatiotemporal input data while simultaneously reducing the statistical dependencies between its outputs, as advocated by the redundancy reduction principle. This approach extends existing models to incorporate temporal aspects of sequences of images of natural scenes