Non-negative Approximation with Thresholding for Cortical Visual Representation

This paper presents a neurally plausible algorithm for the representation of visual inputs by cortical neurons. It has been demonstrated in previous theoretical studies that the main goal of the encoding of the input from lateral geniculate nucleus (LGN) by simple cell is to minimize the representation error. Based on the existing methods, we propose a non-negative approximation algorithm using thresholding. We validate the algorithm via simulation of several known response properties of simple cells, including the sharp and contrast invariant orientation tuning and surround suppression, and as cross orientation suppression.