Rare neural correlations implement robotic conditioning with delayed rewards and disturbances
Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsib...
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Published in | Frontiers in neurorobotics Vol. 7; p. 6 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Research Foundation
01.01.2013
Frontiers Media S.A |
Subjects | |
Online Access | Get full text |
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Summary: | Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Denis Sheynikhovich, Universite Pierre et Marie Curie, France; Eiji Uchibe, Okinawa Institute of Science and Technology, Japan Edited by: Jeffrey L. Krichmar, University of California Irvine, USA |
ISSN: | 1662-5218 1662-5218 |
DOI: | 10.3389/fnbot.2013.00006 |