NDLI: Modeling neural plasticity in echo state networks for time series prediction

Content Provider	IEEE Xplore Digital Library
Author	Yusoff, M.-H. Yaochu Jin
Copyright Year	2014
Description	Author affiliation: Dept. of Comput., Univ. of Surrey, Guildford, UK (Yusoff, M.-H.; Yaochu Jin)
Abstract	In this paper, we investigate the influence of neural plasticity on the learning performance of echo state networks (ESNs) and supervised learning algorithms in training readout connections for two time series prediction problems including the sunspot time series and the Mackey Glass chaotic system. We implement two different plasticity rules that are expected to improve the prediction performance, namely, anti-Oja learning rule and the Bienenstock-Cooper-Munro (BCM) learning rule combined with both offline and online learning of the readout connections. Our experimental results have demonstrated that the neural plasticity can more significantly enhance the learning in offline learning than in online learning.
Starting Page	1
Ending Page	7
File Size	459110
Page Count	7
File Format	PDF
ISBN	9781479955381
DOI	10.1109/UKCI.2014.6930163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-08
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Learning algorithms Computational modeling Neurons Time series analysis Supervised learning Synaptic Plasticity Echo State Networks Predictive models Reservoirs
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Modeling neural plasticity in echo state networks for time series prediction