Self-organizing models develop realistic cortical structures when given approximations of the visual environment as input, and are an effective way to model the development of face recognition abilities. However, environment-driven self-organization alone cannot account for the fact that newborn human infants will preferentially attend to face-like stimuli even immediately after birth. 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 on self-organization as does the external environment. Internal pattern generators 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 model show that such preorganization can account for newborn face preferences, providing a computational framework for examining how genetic influences interact with experience to construct a complex system.

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In Proceedings of the 17th National Conference on Artificial Intelligence and the 12th Annual Conference on Innovative Applications of Artificial Intelligence, pp. 117-122 2000.

@InProceedings{bednar:aaai00, title={Self-Organization Of Innate Face Preferences: Could Genetics Be Expressed Through Learning?}, author={James A. Bednar</a> and Risto Miikkulainen}, booktitle={Proceedings of the 17th National Conference on Artificial Intelligence and the 12th Annual Conference on Innovative Applications of Artificial Intelligence}, pages={117-122}, url="http://www.cs.utexas.edu/users/ai-lab?bednar:aaai00", year={2000} }