Show simple item record  

dc.contributor.authorLobo, Jesus L.en_NZ
dc.contributor.authorDel Ser, Javieren_NZ
dc.contributor.authorBifet, Alberten_NZ
dc.contributor.authorKasabov, Nikolaen_NZ
dc.date.accessioned2019-10-16T21:18:45Z
dc.date.available2020en_NZ
dc.date.available2019-10-16T21:18:45Z
dc.date.issued2020en_NZ
dc.identifier.citationLobo, J. L., Del Ser, J., Bifet, A., & Kasabov, N. (2020). Spiking Neural Networks and online learning: An overview and perspectives. Neural Networks, 121, 88–100. https://doi.org/10.1016/j.neunet.2019.09.004en
dc.identifier.issn0893-6080en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12963
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.en_NZ
dc.language.isoen
dc.rightsThis is an author’s accepted version of an article published in the journal: Neural Networks. © 2019 Elsevier.
dc.subjectcomputer scienceen_NZ
dc.subjectonline learningen_NZ
dc.subjectSpiking Neural Networksen_NZ
dc.subjectstream dataen_NZ
dc.subjectconcept driften_NZ
dc.subjectMachine learning
dc.titleSpiking Neural Networks and online learning: An overview and perspectivesen_NZ
dc.typeJournal Article
dc.identifier.doi10.1016/j.neunet.2019.09.004en_NZ
dc.relation.isPartOfNeural Networksen_NZ
pubs.begin-page88
pubs.elements-id241078
pubs.end-page100
pubs.publication-statusPublisheden_NZ
pubs.volume121en_NZ
dc.identifier.eissn1879-2782en_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record