Abstract
It is known that neurons in the primary visual cortex are characterized by receptive fields that are selective for speciftc spatial orientations. 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. This approach extends existing models to incorporate temporal aspects of sequences of images of natural scenes.

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