Extracted Memory from Temporal Patterns Using Adaptive Resonance and Recurrent Network

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dc.contributor.author Araki, Osamu en_US
dc.date.accessioned 2011-11-14T18:19:23Z
dc.date.available 2011-11-14T18:19:23Z
dc.date.issued 1993-05 en_US
dc.identifier.uri http://hdl.handle.net/2144/2019
dc.description.abstract We can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction. en_US
dc.description.sponsorship Matsushita Electric Industrial Co., Ltd., Tokyo Information Systems Research Laboratory, Tokyo, Japan en_US
dc.publisher Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems en_US
dc.relation.ispartofseries BU CAS/CNS Technical Reports;CAS/CNS-TR-1993-042 en_US
dc.rights Copyright 1993 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission. en_US
dc.title Extracted Memory from Temporal Patterns Using Adaptive Resonance and Recurrent Network en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US

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