Optoelectronic neural networks and learning machines

A brief neural-net primer based on phase-space and energy landscape considerations is presented. This provides the basis for subsequent discussion of optoelectronic architectures and implementations with self-organization and learning ability that are configured around an optical crossbar interconne...

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Bibliographic Details
Published in:IEEE circuits and devices magazine 1989-09, Vol.5 (5), p.32-41
Main Author: Farhat, N.
Format: Article
Language:English
Subjects:
426000 - Engineering- Components, Electron Devices & Circuits- (1990-)
990200 - Mathematics & Computers
ANALOG COMPUTERS
ANIMAL CELLS
ARRAY PROCESSORS
Biomedical optical imaging
COMPUTER ARCHITECTURE
COMPUTER CALCULATIONS
COMPUTER NETWORKS
COMPUTERS
DESIGN
ELECTRO-OPTICAL EFFECTS
ELECTRONIC CIRCUITS
ENGINEERING
GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
INTEGRATED CIRCUITS
Integrated optoelectronics
LAN interconnection
Large scale integration
LEARNING
Machine learning
MICROELECTRONIC CIRCUITS
NERVE CELLS
Neural networks
NONLINEAR PROBLEMS
Optical computing
Optical interconnections
Optical noise
OPTICS
PARALLEL PROCESSING
PROGRAMMING
SOMATIC CELLS
Stochastic resonance
TWO-DIMENSIONAL CALCULATIONS
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Summary:A brief neural-net primer based on phase-space and energy landscape considerations is presented. This provides the basis for subsequent discussion of optoelectronic architectures and implementations with self-organization and learning ability that are configured around an optical crossbar interconnect. Stochastic learning in the context of a Boltzmann machine is described to illustrate the flexibility of optoelectronics in performing tasks that may be difficult for electronics alone. Stochastic nets are studied to gain insight into the possible role of noise in biological neural nets. A description is given of two approaches to realizing large-scale optoelectronic neurocomputers: integrated optoelectronic neural chips with interchip optical interconnects that allow their clustering into large neural networks, and nets with a two-dimensional rather than one-dimensional arrangement of neurons and four-dimensional connectivity matrices for increased packing density and compatibility with two-dimensional data.< >
ISSN:8755-3996
1558-1888
DOI:10.1109/101.34898