Simple and Complex cells in a large-scale neuronal network model of primary visual cortex
New York University
Orientationally selective neurons in the primary visual cortex are generally separated into two broad classes: those that show approximately linear responses (Simple cells) and those that do not (Complex cells). These response differences may underlie important functional differences in visual processing, yet there has been no satisfactory explanation of how these differences arise within the cortex. In this talk, I offer a mechanism within the framework of a large-scale neuronal network model, which is based upon the cortical anatomy and physiology of the macaque primary visual cortex. My results suggest that the Simple-Complex classification reflects different synaptic balances and drives within the same basic model circuit. Since the dichotomy of ``Simple'' and ``Complex'' behavior is seen in other areas of visual processing, the basic explanation may be widely applicable.