Back-propagation learning and nonidealities in analog neural network hardware.

Experimental results from adaptive learning using an optically controlled neural network are presented. The authors have used example problems in nonlinear system identification and signal prediction, two areas of potential neural network application, to study the capabilities of analog neural hardware. These experiments investigated the effects of a variety of nonidealities typical of analog hardware systems. They show that network using large arrays of nonuniform components can perform analog communications with a much higher degree of accuracy than might be expected given the degree of variation in the network's elements. The effects of other common nonidealities, such as noise, weight quantization, and dynamic range limitations, were also investigated.

Signal prediction by an optically controlled neural network.

Networks of interconnected nonlinear analog processors, or neurons, are finding increasing use in adaptive problems. Adaptive signal prediction has been widely used for many years but has been primarily restricted to linear systems and signals, for which the mathematical treatment of the problems is tractable. We present results using an optically controlled adaptive neural network for nonlinear signal prediction.

Amorphous silicon photoconductive arrays for artificial neural networks.

We have demonstrated an electronic implementation of an artificial neural network with 14,400 synaptic connections of variable strength using an array of a:Si:H photoconductors. This neural network has been configured as a Hopfield associative memory, and used to successfully perform simple pattern recognition. Our initial results suggested that, using these a-Si:H photoconductive arrays as the optically programmable synaptic matrix, neural networks of large sizes may be achieved. This paper describes the fabrication and device characteristics of a-Si:H photoconductive arrays as well as a model application of a neural network.