Computational design and nonlinear dynamics of a recurrent network model of the primary visual cortex

Recurrent interactions in the primary visual cortex make its output a compl ex nonlinear transform of its input. This transform serves preattentive vis ual segmentation, that is, autonomously processing visual inputs to give ou tputs that selectively emphasize certain features for segmentation. An anal ytical understanding of the nonlinear dynamics of the recurrent neural circ uit is essential to harness its computational power. We derive requirements on the neural architecture, components, and connection weights of a biolog ically plausible model of the cortex such that region segmentation, figure- ground segregation, and contour enhancement can be achieved simultaneously. In addition, we analyze the conditions governing neural oscillations, illu sory contours, and the absence of visual hallucinations. Many of our analyt ical techniques can be applied to other recurrent networks with translation -invariant neural and connection structures.