A substrate-independent framework to characterize reservoir computers
- Submitting institution
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University of York
- Unit of assessment
- 12 - Engineering
- Output identifier
- 63762705
- Type
- D - Journal article
- DOI
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10.1098/rspa.2018.0723
- Title of journal
- Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences
- Article number
- 20180723
- First page
- -
- Volume
- 475
- Issue
- 2226
- ISSN
- 1364-5021
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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3
- Research group(s)
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B - Intelligent Systems and Nano-Science
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first experimental framework for assessing the potential of new nano-materials as beyond-silicon computing, with novelty in using task-independent metrics based on reservoir computing. Methodology in use at NTNU (neural cultures, Contact: Project PI, NTNU), Sheffield (nano-magnets, Contact: Project PI, Sheffield) and Twente (gold nano-particles, Contact: Project PI, Twente). This work resulted from DSTL funding for material-based computing, and led to EPSRC SpInspired (EP/R032823/1, £500k) and MARCH (EP/V006029/1, £1.15M) projects, and to York leading in EPSRC’s Co-creation theme enabling cross- and interdisciplinary research (Contact: EPSRC Theme Lead).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -