Holography in artificial neural networks

Demetri Psaltis; David Brady; Xiang Guang Gu; Steven Lin

doi:10.1038/343325a0

Holography in artificial neural networks

Demetri Psaltis
, David Brady
, Xiang Guang Gu
, Steven Lin

Research output: Contribution to journal › Review article › peer-review

258 Scopus citations

Abstract

The dense interconnections that characterize neural networks are most readily implemented using optical signal processing. Optoelectronic 'neurons' fabricated from semiconducting materials can be connected by holographic images recorded in photorefractive crystals. Processes such as learning can be demonstrated using holographic optical neural networks.

Original language	English (US)
Pages (from-to)	325-330
Number of pages	6
Journal	Nature
Volume	343
Issue number	6256
DOIs	https://doi.org/10.1038/343325a0
State	Published - 1990
Externally published	Yes

ASJC Scopus subject areas

General

Access to Document

10.1038/343325a0

Cite this

@article{26dab248940a407ebb2afeef5b3e005a,

title = "Holography in artificial neural networks",

abstract = "The dense interconnections that characterize neural networks are most readily implemented using optical signal processing. Optoelectronic 'neurons' fabricated from semiconducting materials can be connected by holographic images recorded in photorefractive crystals. Processes such as learning can be demonstrated using holographic optical neural networks.",

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

year = "1990",

doi = "10.1038/343325a0",

language = "English (US)",

volume = "343",

pages = "325--330",

journal = "Nature",

issn = "0028-0836",

publisher = "Nature Research",

number = "6256",

}

TY - JOUR

T1 - Holography in artificial neural networks

AU - Psaltis, Demetri

AU - Brady, David

AU - Gu, Xiang Guang

AU - Lin, Steven

PY - 1990

Y1 - 1990

N2 - The dense interconnections that characterize neural networks are most readily implemented using optical signal processing. Optoelectronic 'neurons' fabricated from semiconducting materials can be connected by holographic images recorded in photorefractive crystals. Processes such as learning can be demonstrated using holographic optical neural networks.

AB - The dense interconnections that characterize neural networks are most readily implemented using optical signal processing. Optoelectronic 'neurons' fabricated from semiconducting materials can be connected by holographic images recorded in photorefractive crystals. Processes such as learning can be demonstrated using holographic optical neural networks.

UR - https://www.scopus.com/pages/publications/0025060787

UR - https://www.scopus.com/pages/publications/0025060787#tab=citedBy

U2 - 10.1038/343325a0

DO - 10.1038/343325a0

M3 - Review article

C2 - 2300184

AN - SCOPUS:0025060787

SN - 0028-0836

VL - 343

SP - 325

EP - 330

JO - Nature

JF - Nature

IS - 6256

ER -

Holography in artificial neural networks

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this