Encoding multiple orientations in a recurrent network
Models containing recurrent connections amongst the cells within a population can account for a range of empirical data on orientation selectivity in striate cortex. However, existing recurrent models are unable to veridically encode more than one orientation at a time. Underlying this inability is an inherent limitation in the variety of activity profiles that can be stably maintained. We propose a new recurrent model that can form a broader range of stable population activity patterns. We demonstrate that these patterns preserve information about multiple orientations present in the population inputs. This preservation has significant computational consequences when information encoded in several populations must be integrated to perform behavioral tasks, such as visual discrimination.
orientation selectivity population codes recurrent network models