Abstract

A compact optical architecture capable of implementing a number of adaptive neural net models is described. Compact architectures do not involve traditional optical imaging systems and are potentially rugged, easily constructed, and scalable. The critical devices of the generic architecture include 1-D electrooptic modulators and detectors to implement the neural processing elements along with the Pockels readout optical modulator to encode the analog weights. The architectural advances include an input-output compatible method of handling both positive and negative values for the elements of the neuron activation vectors and the synaptic weight matrices during learning and recall. These hybrid architectures are capable of implementing linear and nonlinear associators with adaptive learning algorithms.

© 1988 Optical Society of America