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dc.contributor.authorLobo, JLen_NZ
dc.contributor.authorSer, JDen_NZ
dc.contributor.authorBifet, Aen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.date.accessioned2022-02-04T03:21:23Z
dc.date.available2022-02-04T03:21:23Z
dc.date.copyright2020en_NZ
dc.identifier.citationNeural Networks, Volume 121, January 2020, Pages 88-100.
dc.identifier.issn0893-6080en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/14882
dc.description.abstractApplications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments, and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks. Besides, some specific flavors of Spiking Neural Networks can overcome the necessity of retraining after a drift occurs. This work intends to merge both fields by serving as a comprehensive overview, motivating further developments that embrace Spiking Neural Networks for online learning scenarios, and being a friendly entry point for non-experts.
dc.publisherElsevier
dc.relation.urihttps://www.sciencedirect.com/science/article/abs/pii/S0893608019302655
dc.rightsCopyright © 2020 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version) .
dc.subjectOnline learning; Spiking Neural Networks; Stream data; Concept drift
dc.titleSpiking Neural Networks and Online Learning: An Overview and Perspectivesen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1016/j.neunet.2019.09.004en_NZ
aut.relation.endpage100
aut.relation.startpage88
aut.relation.volume121en_NZ
pubs.elements-id368940
aut.relation.journalNeural Networksen_NZ


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