Abstract

A coherent optical neural network is proposed that has the learning ability to achieve desirable phase values in the frequency domain. It is composed of multiple optical-path differences whose lengths are different from one another. The system learns a phase value at each discrete position in the frequency domain by obeying the complex-valued Hebbian rule. The learning curve also agrees with theoretical evolution.

© 2003 Optical Society of America

Frequency-multiplexed logic circuit based on a coherent optical neural network

Sotaro Kawata and Akira Hirose
Appl. Opt. 44(19) 4053-4059 (2005)

Coherent optical neural networks that have optical-frequency-controlled behavior and generalization ability in the frequency domain

Akira Hirose and Rolf Eckmiller
Appl. Opt. 35(5) 836-843 (1996)

Generation of entanglement between frequency bands by nonlinear fiber propagation and spectral pulse shaping

Daisuke Fujishima, Fumihiko Kannari, Masahiro Takeoka, and Masahide Sasaki
Opt. Lett. 28(4) 275-277 (2003)

Optical sectioning in wide-field microscopy obtained by dynamic structured light illumination and detection based on a smart pixel detector array

Jelena Mitić, Tiemo Anhut, Matthias Meier, Mathieu Ducros, Alexander Serov, and Theo Lasser
Opt. Lett. 28(9) 698-700 (2003)

All-optical neural-net-like image processing with photosensitive nonlinear nematic film

I. C. Khoo, K. Chen, and A. Diaz
Opt. Lett. 28(23) 2372-2374 (2003)