Physical reservoir computing: a tutorial

被引:0
|
作者
Stepney, Susan [1 ]
机构
[1] Univ York, Dept Comp Sci, York YO10 5DD, England
基金
英国工程与自然科学研究理事会;
关键词
Reservoir computing; Physical computing; Echo State Network; NETWORKS; CHAOS;
D O I
10.1007/s11047-024-09997-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:665 / 685
页数:21
相关论文
共 50 条
  • [1] Reservoir computing benchmarks: a tutorial review and critique
    Wringe, Chester
    Trefzer, Martin
    Stepney, Susan
    INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS, 2025,
  • [2] Physical reservoir computing-an introductory perspective
    Nakajima, Kohei
    JAPANESE JOURNAL OF APPLIED PHYSICS, 2020, 59 (06)
  • [3] Pruning and regularization in reservoir computing
    Dutoit, X.
    Schrauwen, B.
    Van Campenhout, J.
    Stroobandt, D.
    Van Brussel, H.
    Nuttin, M.
    NEUROCOMPUTING, 2009, 72 (7-9) : 1534 - 1546
  • [4] Tradeoffs with physical delay feedback reservoir computing
    Gan, Tian
    Stepney, Susan
    Trefzer, Martin A.
    2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021), 2021,
  • [5] Recent advances in physical reservoir computing: A review
    Tanaka, Gouhei
    Yamane, Toshiyuki
    Heroux, Jean Benoit
    Nakane, Ryosho
    Kanazawa, Naoki
    Takeda, Seiji
    Numata, Hidetoshi
    Nakano, Daiju
    Hirose, Akira
    NEURAL NETWORKS, 2019, 115 : 100 - 123
  • [6] From 'follow the leader' to autonomous swarming: physical reservoir computing in two dimensions
    Heywood, Zachary
    Mallinson, Joshua
    Bones, Philip
    Brown, Simon
    NEUROMORPHIC COMPUTING AND ENGINEERING, 2024, 4 (03):
  • [7] Numerical Study on Physical Reservoir Computing With Josephson Junctions
    Watanabe, Kohki
    Mizugaki, Yoshinao
    Moriya, Satoshi
    Yamamoto, Hideaki
    Yamashita, Taro
    Sato, Shigeo
    IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, 2024, 34 (03) : 1 - 4
  • [8] Physical Implementation of Reservoir Computing through Electrochemical Reaction
    Kan, Shaohua
    Nakajima, Kohei
    Asai, Tetsuya
    Akai-Kasaya, Megumi
    ADVANCED SCIENCE, 2022, 9 (06)
  • [9] Physical Reservoir Computing Based on Nanoscale Materials and Devices
    Qi, Zhiying
    Mi, Linjie
    Qian, Haoran
    Zheng, Weiguo
    Guo, Yao
    Chai, Yang
    ADVANCED FUNCTIONAL MATERIALS, 2023,
  • [10] Physical Reservoir Computing Based on Nanoscale Materials and Devices
    Qi, Zhiying
    Mi, Linjie
    Qian, Haoran
    Zheng, Weiguo
    Guo, Yao
    Chai, Yang
    ADVANCED FUNCTIONAL MATERIALS, 2023, 33 (43)