Spiking Neural Networks and online learning: An overview and perspectives

Jesus Lobo, Javier Del Ser , Albert Bifet, Nikola Kasabov

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Applications 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.
Original languageEnglish
Pages (from-to)88-100
Number of pages13
JournalNeural Networks
Volume121
Early online date7 Sep 2019
DOIs
Publication statusPublished - 5 Jan 2020

Keywords

  • On-line learning
  • Concept Drift
  • spiking neural networks
  • stream data

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