Suppression of Related Independent Causes Using An Unsupervised Artificial Neural Network

Abstract

In this paper, we study the problem of identification of underlying causes from a mixture of causes when these causes interfere with one another in a particular manner: the causes we consider are visual inputs to a neural network system which are created by independent underlying causes which may occlude each other. The prototypical problem in this area is a mixture of horizontal and vertical bars in which each horizontal bar interferes with the representation of each vertical bar and vice versa. We develop an unsupervised neural network which is able to identify a single orientation of bars from a mixture of horizontal and vertical bars.