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dc.contributor.authorWilliamson, James R.en_US
dc.date.accessioned2011-11-14T19:00:15Z
dc.date.available2011-11-14T19:00:15Z
dc.date.issued1999-10
dc.identifier.urihttps://hdl.handle.net/2144/2244
dc.description.abstractThis paper proposes a biologically-motivated neural network model of supervised learning. The model possesses two novel learning mechanisms. The first is a network for learning topographic mixtures. The network's internal category nodes are the mixture components, which learn to encode smooth distributions in the input space by taking advantage of topography in the input feature maps. The second mechanism is an attentional biasing feedback circuit. When the network makes an incorrect output prediction, this feedback circuit modulates the learning rates of the category nodes, by amounts based on the sharpness of their tuning, in order to improve the network's prediction accuracy. The network is evaluated on several standard classification benchmarks and shown to perform well in comparison to other classifiers. Possible relationships are discussed between the network's learning properties and those of biological neural networks. Possible future extensions of the network are also discussed.en_US
dc.description.sponsorshipDefense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409)en_US
dc.language.isoen_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-1999-027
dc.rightsCopyright 1999 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.titleSelf-Organization of Topographic Mixture Networks Using Attentional Feedbacken_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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