Multilayer Optical Learning Networks

Kelvin Wagner; Demetri Psaltis

doi:10.1117/12.939913

11 August 1987 Multilayer Optical Learning Networks

Kelvin Wagner, Demetri Psaltis

Proceedings Volume 0752, Digital Optical Computing; (1987) https://doi.org/10.1117/12.939913
Event: OE LASE'87 and EO Imaging Symposium, 1987, Los Angeles, CA, United States

Abstract

In this paper we present a new approach to learning in a multilayer optical neural network which is based on holographically interconnected nonlinear Fabry-Perot etalons. The network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self aligning fashion, as volume holographic gratings in photorefractive crystals. Parallel arrays of globally space integrated inner products diffracted by the interconnecting hologram illuminate arrays of nonlinear Fabry-Perot etalons for fast thresholding of the transformed patterns. A phase conjugated reference wave interferes with a backwards propagating error signal to form holographic interference patterns which are time integrated in the volume of the photorefractive crystal in order to slowly modify and learn the appropriate self aligning interconnections. A holographic implementation of a single layer perceptron learning procedure is presented that can be extendept ,to a multilayer learning network through an optical implementation of the backward error propagation (BEP) algorithm.

Citation Download Citation

Kelvin Wagner and Demetri Psaltis "Multilayer Optical Learning Networks", Proc. SPIE 0752, Digital Optical Computing, (11 August 1987); https://doi.org/10.1117/12.939913

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