Abstract
The mammalian visual system has a hierarchic structure with extensive reciprocal connections. A model is proposed in which the feedback pathways serve to modify afferent sensory stimuli in ways that enhance and complete sensory input patterns, suppress irrelevant features, and generate quasi-sensory patterns when afferent stimulation is weak or absent. Such inversion of sensory coding and feature extraction can be achieved by optimization processes in which scalar responses derived from high-level neural analyzers are used as cost functions to modify the filter properties of more peripheral sensory relays. An optimization algorithm, Alopex, which is used in the model, is readily implemented with known neural circuitry. The functioning of the system is investigated by computer simulations.
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