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Physical reservoir computing -- An introductory perspective

arXiv 2005.00992 ↗First posted 3 May 2020nlin.AO cs.LG physics.app-phcited by 58

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Abstract (arXiv)

Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to exploit the complex dynamics of physical systems as information-processing devices. This framework is particularly suited for edge computing devices, in which information processing is incorporated at the edge (e.g., into sensors) in a decentralized manner to reduce the adaptation delay caused by data transmission overhead. This paper aims to illustrate the potentials of the framework using examples from soft robotics and to provide a concise overview focusing on the basic motivations for introducing it, which stem from a number of fields, including machine learning, nonlinear dynamical systems, biological science, materials science, and physics.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 May 2020.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 10; 2024: 14; 2025: 16; 2026: 10.

58 papers.

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  1. 2024Training of Physical Neural Networkscited by 47
  2. 2023Analogue and Physical Reservoir Computing Using Water Wavescited by 16
  3. 2022Biological neurons act as generalization filters in reservoir computingcited by 12
  4. 2021Learning Temporal Quantum Tomographycited by 9
  5. 2024Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspectivecited by 9
  6. 2022Exploring the limits of multifunctionality across different reservoir computerscited by 7
  7. 2024Reservoir Computing Using Measurement-Controlled Quantum Dynamicscited by 7
  8. 2024Extending echo state property for quantum reservoir computingcited by 7
  9. 2021Neural Echo State Network using oscillations of gas bubbles in watercited by 6
  10. 2023Learning Reservoir Dynamics with Temporal Self-Modulationcited by 4
  11. 2025Confabulation dynamics in a reservoir computer: Filling in the gaps with untrained attractorscited by 4
  12. 2023Harnessing Synthetic Active Particles for Physical Reservoir Computingcited by 3
38 more
  1. 2024Designing Chaotic Attractors: A Semi-supervised Approachcited by 3
  2. 2025Stochastic dynamics learning with state-space systemscited by 3
  3. 2021Transient Chaos in BERTcited by 2
  4. 2022Quantum Noise-Induced Reservoir Computingcited by 2
  5. 2022Quantum-Classical Hybrid Information Processing via a Single Quantum Systemcited by 2
  6. 2023Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Taskscited by 2
  7. 2023Learning unidirectional coupling using echo-state networkcited by 2
  8. 2023Biomembrane-based Memcapacitive Reservoir Computing System for Energy Efficient Temporal Data Processingcited by 2
  9. 2023Universal Mechanical Polycomputation in Granular Mattercited by 2
  10. 2024Exploiting Chaotic Dynamics as Deep Neural Networkscited by 2
  11. 2024How noise affects memory in linear recurrent networkscited by 2
  12. 2025Quantum Reservoir Computing and Risk Boundscited by 2
  13. 2025Multifunctional physical reservoir computing in soft tensegrity robotscited by 2
  14. 2025Growing Reservoirs with Developmental Graph Cellular Automatacited by 2
  15. 2025Echoes of the Past: A Unified Perspective on Fading memory and Echo Statescited by 2
  16. 2025Self-Organising Memristive Networks as Physical Learning Systemscited by 2
  17. 2023Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoircited by 1
  18. 2024Thermodynamic limit in learning period threecited by 1
  19. 2024Hybrid Magnonic Reservoir Computingcited by 1
  20. 2024Echo State network for coarsening dynamics of charge density wavescited by 1
  21. 2025Unsupervised Learning in Echo State Networks for Input Reconstructioncited by 1
  22. 2025Incorporating Coupling Knowledge into Echo State Networks for Learning Spatiotemporally Chaotic Dynamicscited by 1
  23. 2026Local learning for stable backpropagation-free neural network training towards physical learningcited by 1
  24. 2026What Makes a Bacterial Model a Good Reservoir Computer? Predicting Performance from Separability and Similaritycited by 1
  25. 2026Lindblad-Inspired Multi-Timescale Reservoir Computing with Separable Rotation and Dissipationcited by 1
  26. 2022Physical Deep Learning with Biologically Plausible Training Methodcited by 0
  27. 2023Information processing via human soft tissuecited by 0
  28. 2023Forecasting Crude Oil Prices Using Reservoir Computing Modelscited by 0
  29. 2024Gradient-based Design of Computational Granular Crystalscited by 0
  30. 2024Intrinsic Voltage Offsets in Memcapacitive Bio-Membranes Enable High-Performance Physical Reservoir Computingcited by 0
  31. 2024Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computingcited by 0
  32. 2024Neuromorphic Spintronicscited by 0
  33. 2025Harnessing omnipresent oscillator networks as computational resourcecited by 0
  34. 2025Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applicationscited by 0
  35. 2025Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognitioncited by 0
  36. 2025Attractor-merging Crises and Intermittency in Reservoir Computingcited by 0
  37. 2025A tensor network approach for chaotic time series predictioncited by 0
  38. 2025Resonant-Tunnelling Diode Reservoir Computing System for Image Recognitioncited by 0

50 most cited of 58 All 58 (JSON)