Dale, Matthew, Miller, Julian Francis orcid.org/0000-0002-7692-9655, Stepney, Susan orcid.org/0000-0003-3146-5401 et al. (1 more author) (2019) A substrate-independent framework to characterize reservoir computers. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 20180723. ISSN 1364-5021
Abstract
The Reservoir Computing (RC) framework states that any non-linear, input-driven dynamical system (the reservoir) exhibiting properties such as a fading memory and input separability can be trained to perform computational tasks. This broad inclusion of systems has led to many new physical substrates for RC. Properties essential for reservoirs to compute are tuned through reconfiguration of the substrate, such as change in virtual topology or physical morphology. As a result, each substrate possesses a unique "quality" - obtained through reconfiguration - to realise different reservoirs for different tasks. Here we describe an experimental framework to characterise the quality of potentially \textit{any} substrate for RC. Our framework reveals that a definition of quality is not only useful to compare substrates, but can help map the non-trivial relationship between properties and task performance. In the wider context, the framework offers a greater understanding as to what makes a dynamical system compute, helping improve the design of future substrates for RC.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. |
Keywords: | unconventional computing,evolution in materio,reservoir computing,Carbon Nanotubes (CNTs),Characterization,Physical computation,Reservoir computing |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 23 May 2019 09:40 |
Last Modified: | 20 Mar 2025 00:08 |
Published Version: | https://doi.org/10.1098/rspa.2018.0723 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1098/rspa.2018.0723 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146494 |