AN OPTOELECTRONIC ARCHITECTURE FOR MULTILAYER LEARNING IN A SINGLE PHOTOREFRACTIVE CRYSTAL Carsten Peterson, Stephen Redfield, James D. Keeler and Eric Hartman Abstract: We propose a simple architecture for implementing supervised neural network models optically with photorefractive technology. The architecture is very versatile: a wide range of supervised learning algorithms can be implemented including mean-field-theory, backpropagation, and Kanerva-style networks. Our architecture is based on a single crystal with spatial multiplexing, rather than the more commonly used angular multiplexing. It handles hidden units and places no restrictions on connectivity. Associated with spatial multiplexing are certain physical phenomena, rescattering and beam depletion, which tend to degrade the matrix multiplication. Detailed simulations including beam absorbtion and grating decay show that the supervised learning algorithms (slightly modified) compensate for these degradations.