Physical reservoir computing: a tutorial

Abstract This tutorial covers physical reservoir computing from a computer science perspective. It first defines what it means for a physical system to compute, rather than merely evolve under the laws of physics. It describes the underlying computational model, the Echo State Network (ESN), and also some variants designed to make physical implementation easier. It explains why the ESN model is particularly suitable for direct physical implementation. It then discusses the issues around choosing a suitable material substrate, and interfacing the inputs and outputs. It describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures. It covers optimising configuration parameters, exploring the space of potential configurations, and simulating the physical reservoir. It ends with a look at the future of physical reservoir computing as devices get more powerful, and are integrated into larger systems.

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Publication Details

Journal
Natural Computing
Published
2024-11-15
DOI
https://doi.org/10.1007/s11047-024-09997-y
Citations
18
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
6.25

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article

Physical reservoir computing: a tutorial

Susan Stepney
18 citations
Natural Computing
Neural Networks and Reservoir Computing
6.25
article

Physical reservoir computing: a tutorial

Susan Stepney
article en
18 citations

Abstract

Abstract This tutorial covers physical reservoir computing from a computer science perspective. It first defines what it means for a physical system to compute, rather than merely evolve under the laws of physics. It describes the underlying computational model, the Echo State Network (ESN), and also some variants designed to make physical implementation easier. It explains why the ESN model is particularly suitable for direct physical implementation. It then discusses the issues around choosing a suitable material substrate, and interfacing the inputs and outputs. It describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures. It covers optimising configuration parameters, exploring the space of potential configurations, and simulating the physical reservoir. It ends with a look at the future of physical reservoir computing as devices get more powerful, and are integrated into larger systems.

Natural ComputingVol. 23(4)
University of York (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 3%
Neural Networks and Reservoir Computing
6.25
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