Generalization of back-propagation to recurrent neural networks

Abstract

An adaptive neural network with asymmetric connections is introduced. This network is related to the Hopfield network with graded neurons and uses a recurrent generalization of the \ensuremath{\delta} rule of Rumelhart, Hinton, and Williams to modify adaptively the synaptic weights. The new network bears a resemblance to the master/slave network of Lapedes and Farber but it is architecturally simpler.