E. A. Rietman

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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(More)
The author report on the design, construction and operation of a hybrid electrooptic computer intended for neural-network applications. They have configured this system to implement what they believe is the largest fully interconnected neural network built to date. The 14400 synapses in this network possess full analog depth and can be dynamically(More)
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(More)
The authors used a synaptic array of amorphous silicon photoconductors to build a feedforward adaptive neural network. Using backpropagation learning, this network can be taught to perform simple tasks of analog computation. The performance of the network compares well with that of an idealized model, despite significant component variation and externally(More)
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