NEURAL IMPLEMENTATION OF BAYESIAN VISION THEORIES BY UNSUPERVISED LEARNING
Biological organisms have to estimate properties of the world from visual signals. Many authors, see [1] and references therein, have suggested that Bayesian estimation theory gives a natural framework for visual perception. Bayesian models exist for such visual abilities as depth perception, object recognition, image segmentation and self-organization. Indeed all of cognition can, in principle, be formulated in Bayesian terms1.