Associative Decorrelation Dynamics: A Theory of Self-Organization and Optimization in Feedback Networks

Abstract

This paper outlines a dynamic theory of development and adap(cid:173) tation in neural networks with feedback connections. Given in(cid:173) put ensemble, the connections change in strength according to an associative learning rule and approach a stable state where the neuronal outputs are decorrelated . We apply this theory to pri(cid:173) mary visual cortex and examine the implications of the dynamical decorrelation of the activities of orientation selective cells by the intracortical connections. The theory gives a unified and quantita(cid:173) tive explanation of the psychophysical experiments on orientation contrast and orientation adaptation. Using only one parameter, we achieve good agreements between the theoretical predictions and the experimental data.