Class specific redundancies in natural images: a theory of extrastriate visual processing

Statistical properties of natural signals is an important factor in forming neuronal selectivities of brain sensory system. One such property is the redundancy in visual and auditory inputs to the brain. In this paper, we introduce the concept of class specific redundancies in natural images and propose that the selectivity of neurons in extrastriate visual areas is developed to reveal these redundancies. In each extrastriate area, a redundancy reduction mechanism removes these revealed redundancies to provide a more efficient representation of the input image. To test this hypothesis, we implemented a model of area V2 and trained this model with a set of natural images. Experiments on artificial stimulus sets and natural images show the close similarity of model neurons to real V2 neurons and their preference for coding object contours over textures.