Optical neural networks using a new radial nonlinear neural layer

Kelvin H. Wagner; Michael Mozer; Paul Smolensky; Yoshiro Miyata; Mike Fellows

doi:10.1117/12.983192

2 February 1993 Optical neural networks using a new radial nonlinear neural layer

Kelvin H. Wagner, Michael Mozer, Paul Smolensky, Yoshiro Miyata, Mike Fellows

Proceedings Volume 1773, Photonics for Computers, Neural Networks, and Memories; (1993) https://doi.org/10.1117/12.983192
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

Radially nonlinear neurons are introduced, and back propagation learning for multilayer networks of these simple hidden units is derived and simulated. The nonlinear transformation performed by a hidden layer of radial units can be represented as a simple multiplication of the summed net input to each neuron by a single value which is only dependent on the total input to the hidden layer. This allows a simple optical implementation, in which a single modulator/detector is able to act as an entire hidden layer by multiplexing the neuron net inputs and processed outputs.

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Kelvin H. Wagner, Michael Mozer, Paul Smolensky, Yoshiro Miyata, and Mike Fellows "Optical neural networks using a new radial nonlinear neural layer", Proc. SPIE 1773, Photonics for Computers, Neural Networks, and Memories, (2 February 1993); https://doi.org/10.1117/12.983192

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KEYWORDS

Neurons

Nonlinear optics

Neural networks

Multiplexing

Absorption

Modulators

Sensors

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