A model of angle selectivity in area V2 with local divisive normalization

Efficient coding hypothesis states that the goal of sensory system of the brain is to remove redundancies in the sensory input. Several models tried to remove redundancy in the visual input and successfully modeled the functional properties of neurons in the primary visual cortex. However there has been no progress to extend these models to simulate the properties of neurons in extrastriate visual areas. In this paper, we propose that visual cortex tries to remove higher order dependencies in a hierarchical architecture. In each layer a nonlinear mechanism removes redundancies in a local neighborhood. We used the biologically plausible divisive normalization mechanism in a two layer model network to remove nonlinear dependencies in the input. Units in this model can simulate the responses of neurons in area V2 to angle stimuli.