A model for the horizontal neural connections in the primary visual cortex

Abstract

A model for the horizontal neural connections in the primary visual cortex is proposed. The model is based on the Gabor function model for the simple cells and the Fourier amplitude model for the complex cells. In this model, the statistical properties of the signal and noise are stored in the horizontal connectivity pattern, which is used as a priori knowledge to extract the signal from noisy input. The method of the Wiener filter is applied to the activity pattern of the model complex cells, and the optimal horizontal connectivity pattern for estimating the signal is derived. As a simple but important example, the model horizontal connections required to extract oriented features, such as lines or edges, are calculated. The results of numerical simulations show that they can successfully extract the local features from degraded input. The obtained model horizontal connectivity turned out to be highly specific in connecting cells having similar orientation selectivity, which is in agreement with the experimental observations of Ts'o et al. (1986).