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The DOI matches its Crossref record; 127 of the 127 references it cites were found in a registry or an index.

Emerging opportunities and challenges for the future of reservoir computing

Open access
Yan, Min, Huang, Can, Bienstman, Peter, Tino, Peter, Lin, Wei, Sun, Jie·2024·Nature Communications
DOI 10.1038/s41467-024-45187-1
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Abstract

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines.

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Metadata

Authors
Yan, M., Huang, C., Bienstman, P., Tino, P., Lin, W., & Sun, J.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-03-06
Volume
15
Issue
1
Pages
2056-2056
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-024-45187-1
OpenAlex
W4392498941
Cited by
346
References
127
FWCI
51.49
Type
journal article
Language
en
Update notices
correction (2024-11-18)
Open access
gold
Licence

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Yan, M., Huang, C., Bienstman, P., Tino, P., Lin, W., & Sun, J. (2024). Emerging opportunities and challenges for the future of reservoir computing. Nature Communications. https://doi.org/10.1038/s41467-024-45187-1

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