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Local learning algorithm for optical neural networks

Qiao, Yong and Psaltis, Demetri (1992) Local learning algorithm for optical neural networks. Applied Optics, 31 (17). pp. 3285-3288. ISSN 0003-6935. http://resolver.caltech.edu/CaltechAUTHORS:QIAao92

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Abstract

An anti-Hebbian local learning algorithm for two-layer optical neural networks is introduced. With this learning rule, the weight update for a certain connection depends only on the input and output of that connection and a global, scalar error signal. Therefore the backpropagation of error signals through the network, as required by the commonly used back error propagation algorithm, is avoided. It still guarantees, however, that the synaptic weights are updated in the error descent direction. With the apparent advantage of simpler optical implementation this learning rule is also shown by simulations to be computationally effective.


Item Type:Article
Additional Information:© Copyright 1992 Optical Society of America Received 30 September, 1991 This work was supported by the Defense Advanced Research Projects Agency and the U.S. Air Force Office of Scientific Research.
Subject Keywords:Optical neural networks, anti-Hebbian local learning
Record Number:CaltechAUTHORS:QIAao92
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:QIAao92
Alternative URL:http://www.opticsinfobase.org/abstract.cfm?URI=ao-31-17-3285
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:3259
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:25 May 2006
Last Modified:26 Dec 2012 08:53

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