BAYESIAN SELF-ORGANIZATION DRIVEN BY PRIOR PROBABILITY-DISTRIBUTIONS

Recent work by Becker and Hinton (1992) shows a promising mechanism, b ased on maximizing mutual information assuming spatial coherence, by w hich a system can self-organize to learn visual abilities such as bino cular stereo. We introduce a more general criterion, based on Bayesian probability theory, and thereby demonstrate a connection to Bayesian theories of visual perception and to other organization principles for early vision (Atick and Redlich 1990). Methods for implementation usi ng variants of stochastic learning are described.