A CNN model of multi-dimensional stimulus selectivity in primary visual cortex

We describe a neuromorphic approach to implementing model visual cortical neurons using a four-layer cellular neural network (CNN) chips. A key challenge is that visual cortical neurons are simultaneously selective along many stimulus dimensions, including retinal position, spatial frequency, orientation, temporal frequency, direction of motion, and binocular disparity. The ubiquity of intra-cortical feedback interconnections also implies that the neurons should operate in parallel and in continuous time. We discuss the modeling and implementation considerations that lead naturally to four layer networks, and describe the current status of our work in building silicon networks of tens of thousands of neurons.