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Pattern-Generator-Driven Development in Self-Organizing Models

By James A. Bednar and Risto Miikkulainen

Abstract

Self-organizing models develop realistic cortical structures when given approximations of the visual environment as input. Recently it has been proposed that internally generated input patterns, such as those found in the developing retina and in PGO waves during REM sleep, may have the same effect. Internal pattern generators would constitute an efficient way to specify, develop, and maintain functionally appropriate perceptual organization. They may help express complex structures from minimal genetic information, and retain this genetic structure within a highly plastic system. Simulations with the RF-LISSOM orientation map model indicate that such preorganization is possible, providing a computational framework for examining how genetic influences interact with visual experience

Topics: Computational Neuroscience, Artificial Intelligence, Complexity Theory, Machine Learning, Neural Nets, Developmental Psychology, Neural Modelling, Neural Modelling
Publisher: Plenum, New York
Year: 1998
OAI identifier: oai:cogprints.org:140
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