A new approach to learning in a multilayer optical neural network based on holographically interconnected nonlinear devices is presented. The proposed 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 backward propagating error signal to form holographic interference patterns which are time integrated in the volume of a photorefractive crystal to modify slowly and learn the appropriate self-aligning interconnections. This multilayer system performs an approximate implementation of the backpropagation learning procedure in a massively parallel high-speed nonlinear optical network.
© 1987 Optical Society of AmericaFull Article | PDF Article
OSA Recommended Articles
Kelvin Wagner and Timothy M. Slagle
Appl. Opt. 32(8) 1408-1435 (1993)
Steven R. Skinner, Elizabeth C. Behrman, Alvaro A. Cruz-Cabrera, and James E. Steck
Appl. Opt. 34(20) 4129-4135 (1995)
Appl. Opt. 26(23) 5104-5111 (1987)