A Cortical Network Model for Early Vision Processing

Abstract

We present an isotropic neural network model for processing by layer IVc of the primate primary visual cortex. It describes how this layer can reconstruct fine local details in an image which have been lost in the low-capacity retinal-LGN pathway, while at the same time narrowing the effective spatial-frequency bandwidth of the response to sinusoidal patterns. We also investigate the circumstances under which such a network can act as an elementary feature extractor by responding preferentially to striped, checked or other high-symmetry patterns. We find that the model can act in something like this way in a particular region of its parameter space, but that such behaviour is incompatible with the reconstruction of local detail.