CNN-based retinal model uncovers a new form of edge enhancement in biological visual processing

Visual processing in the retina is mediated by complex interactions between millions of neurons that shape the visual message in both space and time. Conventional neurobiological method study single cells, and activity is recorded in response to simple stimuli as a function of time. By modeling retina activity using cellular neural network (CNN) one can begin to think about retinal interactions in space/time, and consider the activity of large populations of cells as the deformation of surfaces. Using CNN we predicted a powerful form of edge enhancement mediated by a novel space-time interaction. This is the first known form of edge enhancement that is not mediated by lateral inhibition. So far we have examined this mechanism in salamander retina, but hope to extend these results to mammalian retinas as well