# A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

arXiv: https://arxiv.org/abs/2307.15092 · HTML: https://mlsanity.com/paper/2307.15092/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 337 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 30 of 51)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [A Review on Deep Learning Techniques Applied to Semantic Segmentation](https://mlsanity.com/paper/1704.06857/) (2017)
- [Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?](https://mlsanity.com/paper/1801.03744/) (2018)
- [Reservoir Computing Universality With Stochastic Inputs](https://mlsanity.com/paper/1807.02621/) (2018)
- [Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity](https://mlsanity.com/paper/2107.04084/) (2021)
- [Echo State Networks trained by Tikhonov least squares are L2(μ) approximators of ergodic dynamical systems](https://mlsanity.com/paper/2005.06967/) (2020)
- [Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets](https://mlsanity.com/paper/1901.09049/) (2019)
- [Machine Learning Methods for Attack Detection in the Smart Grid](https://mlsanity.com/paper/1503.06468/) (2015)
- [full-FORCE: A Target-Based Method for Training Recurrent Networks](https://mlsanity.com/paper/1710.03070/) (2017)
- [Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management](https://mlsanity.com/paper/1801.05500/) (2018)
- [Supervised Learning in Spiking Neural Networks with FORCE Training](https://mlsanity.com/paper/1609.02545/) (2016)
- [Evolving Robust Neural Architectures to Defend from Adversarial Attacks](https://mlsanity.com/paper/1906.11667/) (2019)
- [Reinforcement Learning with Convolutional Reservoir Computing](https://mlsanity.com/paper/1912.04161/) (2019)
- [Exploiting short-term memory in soft body dynamics as a computational resource](https://mlsanity.com/paper/1406.4929/) (2014)
- [Reservoir Computing using Cellular Automata](https://mlsanity.com/paper/1410.0162/) (2014)
- [Role of non-linear data processing on speech recognition task in the framework of reservoir computing](https://mlsanity.com/paper/1906.02812/) (2019)
- [Physical Reservoir Computing with Origami and its Application to Robotic Crawling](https://mlsanity.com/paper/2101.08348/) (2021)
- [ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models](https://mlsanity.com/paper/2204.05117/) (2022)
- [DNA Reservoir Computing: A Novel Molecular Computing Approach](https://mlsanity.com/paper/1306.5998/) (2013)
- [Randomly weighted CNNs for (music) audio classification](https://mlsanity.com/paper/1805.00237/) (2018)
- [Continual General Chunking Problem and SyncMap](https://mlsanity.com/paper/2006.07853/) (2020)
- [A Neural Architecture Search based Framework for Liquid State Machine Design](https://mlsanity.com/paper/2004.07864/) (2020)
- [Cellular Automata Can Reduce Memory Requirements of Collective-State Computing](https://mlsanity.com/paper/2010.03585/) (2020)
- [Memristor models for machine learning](https://mlsanity.com/paper/1406.2210/) (2014)
- [Reservoir Computing Using Non-Uniform Binary Cellular Automata](https://mlsanity.com/paper/1702.03812/) (2017)

## Built on by (most cited first, 22 of 22)

- [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)
- [Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data](https://mlsanity.com/paper/2402.03319/) (2024)
- [Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System](https://mlsanity.com/paper/2405.13347/) (2024)
- [Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics](https://mlsanity.com/paper/2312.11834/) (2023)
- [Unsupervised Learning in Echo State Networks for Input Reconstruction](https://mlsanity.com/paper/2501.11409/) (2025)
- [Sign Language Recognition using Parallel Bidirectional Reservoir Computing](https://mlsanity.com/paper/2512.19451/) (2025)
- [Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos](https://mlsanity.com/paper/2606.22765/) (2026)
- [Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction](https://mlsanity.com/paper/2412.17565/) (2024)
- [Learning Music Audio Representations With Limited Data](https://mlsanity.com/paper/2505.06042/) (2025)
- [Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning](https://mlsanity.com/paper/2505.24099/) (2025)
- [ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing](https://mlsanity.com/paper/2506.04249/) (2025)
- [SyncMapV2: Robust and Adaptive Unsupervised Segmentation](https://mlsanity.com/paper/2506.16297/) (2025)
- [Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computing](https://mlsanity.com/paper/2510.14287/) (2025)
- [BOND: License to Train with Black-Box Functions](https://mlsanity.com/paper/2510.25074/) (2025)
- [A Novel Reservoir Computing Framework for Chaotic Time Series Prediction Using Time Delay Embedding and Random Fourier Features](https://mlsanity.com/paper/2511.02877/) (2025)
- [RECAP: Local Hebbian Prototype Learning as a Self-Organizing Readout for Reservoir Dynamics](https://mlsanity.com/paper/2603.06639/) (2026)
- [Lead Zirconate Titanate Reservoir Computing for Classification of Written and Spoken Digits](https://mlsanity.com/paper/2604.00207/) (2026)
- [Bridging Theory and Practice in Crafting Robust Spiking Reservoirs](https://mlsanity.com/paper/2604.06395/) (2026)
- [Vestibular reservoir computing](https://mlsanity.com/paper/2604.09943/) (2026)
- [Scalable Memristive-Friendly Reservoir Computing for Time Series Classification](https://mlsanity.com/paper/2604.19343/) (2026)
- [A Low-Cost Hybrid Reservoir Computing Model for Isolated Sign Language Video Recognition](https://mlsanity.com/paper/2608.03444/) (2026)

## Abstract (arXiv)

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.