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dc.contributor.authorBradski, Garyen_US
dc.contributor.authorCarpenter, Gail A.en_US
dc.contributor.authorGrossberg, Stephenen_US
dc.date.accessioned2011-11-14T18:21:46Z
dc.date.available2011-11-14T18:21:46Z
dc.date.issued1991-02
dc.identifier.urihttps://hdl.handle.net/2144/2062
dc.description.abstractWorking memory neural networks are characterized which encode the invariant temporal order of sequential events that may be presented at widely differing speeds, durations, and interstimulus intervals. This temporal order code is designed to enable all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed in neural architectures which self-organize learned codes for variable-rate speech perception, sensory-motor planning, or 3-D visual object recognition. Using such a working memory, a self-organizing architecture for invariant 3-D visual object recognition is described that is based on the model of Seibert and Waxman [1].en_US
dc.description.sponsorshipAir Force Office of Scientific Research (90-128, 90-0175); British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530, IRI 87-16960)en_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1991-007en_US
dc.rightsCopyright 1991 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.titleWorking memory networks for learning multiple groupings of temporally ordered events: applications to 3-D visual object recognitionen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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