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

The recurrent neural interaction in the primary visual cortex makes its outputs complex nonlinear functions of its inputs. This nonlinear transform serves the role of pre-attentive visual segmentation, i.e., the autonomous transformation from visual inputs to processed outputs that selectively emphasize certain features (e.g., pop-out features) for segmentation. Understanding the nonlinear dynamics of the neural circuit is a key to appreciating the cortical computational potential and tasks. However, the complex nonlinear dynamics of recurrent networks makes it extremely difficult to build a well-behaved and computationally functional model of the cortex merely by simulation trials. This paper describes an analytical study of the recurrent neural dynamics. We derive requirements on the neural architecture, components, and connection weights of a biologically plausible model of the cortex to achieve simultaneously different components of pre-attentive segmentation: region segmentation, figure-ground segregation, and contour enhancement. In addition, we analysis conditions for behaviors such as neural oscillations, illusory contours, and visual halluciations. Many of our analytical techniques can be applied to other recurrent networks with translation invariant neural and connection structures. Computational design and nonlinear dynamics of recurrent network models of the primary visual cortex Zhaoping Li Gatsby Unit