9312345 Skinner The objective of this project is to investigate the usefulness of Kerr- Type nonlinear optical materials for all-optical implementations of feed forward artificial neural networks with optical error back- propagation training. These types of materials have pico-second response times and allow both weighted connections and nonlinear neuron processing to be implemented using thin materials layers separated by free space. This gives such networks and advantage over other optical implementations which require complicated hardware devices to implement the weighted connections and separate hardware for neuron processing. In addition, a layered network of Kerr-Type material can process both forward calculation signals and backward error propagation simultaneously. The nonlinear layers serve as processing layers and the linear layers (fee space) serve as connection layers. Each nonlinear layer can be thought of as a plane continuum of neurons connected with the layers before and after it by a continuum of optical paths. The number of available neurons and connections are limited only by the available resolution of the system optics. The inputs and weights are a distribution of irradiance in 2D space and the weights vary the refractive index profile of the nonlinear medium. The nonlinear forcing and processing functions necessary for neural computing are given by the self-action effects of the propagating wave coupled with the optical with signals applied to the nonlinear layers. Such a network has been numerically simulated in two dimensions and trained to perform logic functions and a simple optical demonstration of this network is constructed. Further simulation and training of the network to preform more complicated tasks as well as implementation of these networks in two dimensions is proposed, followed by an extension of the simulation and implementation to three dimensions. This well involve both theoretical and experimental investigations in parallel with computer simulation to determine the optimal network geometry (number and type of layers, size and required resolution of with and signal fields) as well as better techniques for controlling the application of the weight light fields. ***
