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dc.contributor.authorTu, Een_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.contributor.authorYang, Jen_NZ
dc.date.accessioned2016-04-06T04:38:36Z
dc.date.available2016-04-06T04:38:36Z
dc.date.copyright2016-03-15en_NZ
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, vol.PP, no.99, pp.1-13 doi: 10.1109/TNNLS.2016.2536742en_NZ
dc.identifier.issn2162-2388en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/9661
dc.description.abstractThis paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network (SNN) architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a better accuracy of temporal pattern recognition, a better and earlier event prediction, and a better understanding of complex temporal stream data through visualization of the NeuCube connectivity. The effect of the new mapping is demonstrated on three benchmark problems. The first one is the early prediction of patient sleep stage event from temporal physiological data. The second one is the pattern recognition of dynamic temporal patterns of traffic in the Bay Area of California and the last one is the Challenge 2012 contest data set. In all the cases, the use of the proposed mapping leads to an improved accuracy of pattern recognition and event prediction and a better understanding of the data when compared with traditional machine learning techniques or SNN reservoirs with an arbitrary mapping of the variables.en_NZ
dc.languageENGen_NZ
dc.publisherIEEE
dc.relation.urihttp://dx.doi.org/10.1109/TNNLS.2016.2536742
dc.rightsCopyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectNeuCube architecture; Spiking neural network; Early event prediction; Spatiotemporal data
dc.titleMapping temporal variables into the NeuCube for improved pattern recognition, predictive modeling, and understanding of stream dataen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1109/TNNLS.2016.2536742en_NZ
pubs.elements-id201158


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