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

arXiv: https://arxiv.org/abs/2402.03319 · HTML: https://mlsanity.com/paper/2402.03319/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 119 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 21 of 21)

- [Liquid Time-constant Networks](https://mlsanity.com/paper/2006.04439/) (2020)
- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020)
- [Brain-inspired computing: We need a master plan](https://mlsanity.com/paper/2104.14517/) (2021)
- [Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius](https://mlsanity.com/paper/1910.04426/) (2019)
- [A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware](https://mlsanity.com/paper/2107.03992/) (2021)
- [Self-learning Machines based on Hamiltonian Echo Backpropagation](https://mlsanity.com/paper/2103.04992/) (2021)
- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing](https://mlsanity.com/paper/2203.13294/) (2022)
- [Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons](https://mlsanity.com/paper/1912.07044/) (2019)
- [Analogue and Physical Reservoir Computing Using Water Waves](https://mlsanity.com/paper/2306.09095/) (2023)
- [Progress in Brain Computer Interfaces: Challenges and Trends](https://mlsanity.com/paper/1901.03442/) (2019)
- [Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loops](https://mlsanity.com/paper/2011.10115/) (2020)
- [Unveiling the role of plasticity rules in reservoir computing](https://mlsanity.com/paper/2101.05848/) (2021)
- [Neural Echo State Network using oscillations of gas bubbles in water](https://mlsanity.com/paper/2112.11592/) (2021)
- [Efficient implementations of echo state network cross-validation](https://mlsanity.com/paper/2006.11282/) (2020)
- [A New Paradigm of Reservoir Computing Exploiting Hydrodynamics](https://mlsanity.com/paper/2302.01978/) (2023)
- [Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept](https://mlsanity.com/paper/2303.01801/) (2023)
- [Modular DFR: Digital Delayed Feedback Reservoir Model for Enhancing Design Flexibility](https://mlsanity.com/paper/2307.11094/) (2023)
- [Penetration of action potentials during collision in the median and lateral giant axons of invertebrates](https://mlsanity.com/paper/1404.3643/) (2014)
- [Mechanical Surface Waves Accompany Action Potential Propagation](https://mlsanity.com/paper/1407.7600/) (2014)

## Built on by (most cited first, 5 of 5)

- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://mlsanity.com/paper/2403.01024/) (2024)
- [Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectives](https://mlsanity.com/paper/2503.04978/) (2025)
- [Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime](https://mlsanity.com/paper/2504.07221/) (2025)
- [Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition](https://mlsanity.com/paper/2507.15158/) (2025)

## 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.