mlsanityData to 7 Oct 2026

Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data

arXiv 2402.03319 ↗First posted 3 Jan 2024cs.NE cs.AI nlin.CDcited by 5

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Abstract (arXiv)

Reservoir computing (RC) systems can efficiently forecast chaotic time series using nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper we experimentally validate a physical RC system that substitutes the effect of randomness for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with a minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the `next-generation' improvement of the traditional RC algorithm.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 119 references that are in the map. First posted on arXiv 3 Jan 2024.

Builds on

21 of 119 references.

References · 21

  1. 2020Liquid Time-constant Networkscited by 160
  2. 2021Next Generation Reservoir Computingcited by 160
  3. 2020Photonics for artificial intelligence and neuromorphic computingcited by 111
  4. 2021Brain-inspired computing: We need a master plancited by 51
  5. 2019Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radiuscited by 34
  6. 2021A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardwarecited by 32
  7. 2021Self-learning Machines based on Hamiltonian Echo Backpropagationcited by 28
  8. 2023A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learningcited by 22
  9. 2022Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computingcited by 20
  10. 2019Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitonscited by 19
  11. 2023Analogue and Physical Reservoir Computing Using Water Wavescited by 16
  12. 2019Progress in Brain Computer Interfaces: Challenges and Trendscited by 13
9 more
  1. 2020Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loopscited by 9
  2. 2021Unveiling the role of plasticity rules in reservoir computingcited by 7
  3. 2021Neural Echo State Network using oscillations of gas bubbles in watercited by 6
  4. 2020Efficient implementations of echo state network cross-validationcited by 5
  5. 2023A New Paradigm of Reservoir Computing Exploiting Hydrodynamicscited by 5
  6. 2023Reservoir computing based on solitary-like waves dynamics of film flows: a proof of conceptcited by 5
  7. 2023Modular DFR: Digital Delayed Feedback Reservoir Model for Enhancing Design Flexibilitycited by 2
  8. 2014Penetration of action potentials during collision in the median and lateral giant axons of invertebratescited by 1
  9. 2014Mechanical Surface Waves Accompany Action Potential Propagationcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspectivecited by 9
  2. 2024Reservoir Computing Using Measurement-Controlled Quantum Dynamicscited by 7
  3. 2025Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectivescited by 3
  4. 2025Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regimecited by 2
  5. 2025Resonant-Tunnelling Diode Reservoir Computing System for Image Recognitioncited by 0