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Applied Optics

Applied Optics


  • Vol. 26, Iss. 23 — Dec. 1, 1987
  • pp: 5061–5076

Multilayer optical learning networks

Kelvin Wagner and Demetri Psaltis  »View Author Affiliations

Applied Optics, Vol. 26, Issue 23, pp. 5061-5076 (1987)

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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 America

Original Manuscript: May 28, 1987
Published: December 1, 1987

Kelvin Wagner and Demetri Psaltis, "Multilayer optical learning networks," Appl. Opt. 26, 5061-5076 (1987)

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