Associative Decorrelation Dynamics in Visual Cortex

This paper outlines a dynamic theory of development and adaptation in neural networks with feedback connections. Given input 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 primary visual cortex and examine the implications of the dynamical decorrelation of the activities of cortical cells. The orientation selective columns are developed with both feedforward and feedback connection changes and with natural images as inputs. The feedback connections form columnar structures which regulate the development of orientation selectivity of feedforward connections. The theory gives a uni ed and quantitative account of the development of both feedforward and feedback connections of orientation selective cells which are shown to be able to explain 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.