<title>Magneto-optical neural network image processing system</title>

Abstract

This paper describes the operation and construction of a magneto-optical neural network image processing system, together with a discussion of the physical basis for its operation. We discuss the behavior of the model under simulated annealing in light of statistical physics. This paper also presents results of large scale simulations of the physical system performed on CM- 2 Connection Machine. The system is capable of image recognition, reconstruction, and processing by use of massive parallelism in a physical thin film. A spin glass thin film material, in conjunction with magneto-optical control, implements a Boltzmann Machine like neural network. The thin film provides the units and connective weights of the neural network, and the magneto-optical system controls the image learning and recall by accessing the units and weights, and allowing their modification, using physical annealing in the film. Images are learned sequentially via stochastic minimization of the system energy, a function of all spin orientations and of interspin distances. Images can be recalled later when a similar, corrupted, or noisy version of a learned prototype image is presented. Our Monte Carlo style computer simulations of this system show its feasibility and practicality for real time image recognition.