Photorefractive crystals in optical neural computers

David Brady; Xiang Guang Gu; Demetri Psaltis

doi:10.1117/12.944112

Photorefractive crystals in optical neural computers

David Brady, Xiang Guang Gu, Demetri Psaltis

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

The storage capacity for neural interconnections in photorefractive crystals and the use of the dynamic nature of the photorefractive effect to train these interconnections are discussed. We describe an optical neural architecture which uses the characteristics of the photorefractive response to implement error driven learning and describe a modified perceptron algorithm which we have used to train this optical system.

Original language	English (US)
Pages (from-to)	132-136
Number of pages	5
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	882
DOIs	https://doi.org/10.1117/12.944112
State	Published - May 3 1988
Externally published	Yes

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.944112

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title = "Photorefractive crystals in optical neural computers",

abstract = "The storage capacity for neural interconnections in photorefractive crystals and the use of the dynamic nature of the photorefractive effect to train these interconnections are discussed. We describe an optical neural architecture which uses the characteristics of the photorefractive response to implement error driven learning and describe a modified perceptron algorithm which we have used to train this optical system.",

author = "David Brady and Gu, {Xiang Guang} and Demetri Psaltis",

year = "1988",

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day = "3",

doi = "10.1117/12.944112",

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AU - Gu, Xiang Guang

AU - Psaltis, Demetri

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N2 - The storage capacity for neural interconnections in photorefractive crystals and the use of the dynamic nature of the photorefractive effect to train these interconnections are discussed. We describe an optical neural architecture which uses the characteristics of the photorefractive response to implement error driven learning and describe a modified perceptron algorithm which we have used to train this optical system.

AB - The storage capacity for neural interconnections in photorefractive crystals and the use of the dynamic nature of the photorefractive effect to train these interconnections are discussed. We describe an optical neural architecture which uses the characteristics of the photorefractive response to implement error driven learning and describe a modified perceptron algorithm which we have used to train this optical system.

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Photorefractive crystals in optical neural computers

Abstract

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