Self-learning threshold-controlled neural network

Abstract

A neural network structure is proposed that is controlled by device thresholds rather than multiplicative factors. This network has the feature that the learning parameter is embodied locally in the device thresholds. The network is shown to be capable of learning by example, as well as exhibiting other desirable features of the Hopfield type networks.