Optoelectronic neural networks: mapping multilayer architectures on to an optoelectronic demonstrator

Abstract

In this paper we outline some of the changes needed to implement multilayer feed-forward neural networks using the demonstrator hardware which was based on around an array of vertical cavity surface emitting lasers. Network simulations show that the neural network demonstrator hardware can be used to implement two different classes of feed-forward network, the multilayer perceptron (MLP) and radial basis function (RBF) networks. In both cases, the actual training of the networks is performed offline using hardware simulations and the weighted interconnections between neurons are fixed before application to the optoelectronic hardware.