Reservoir computing is a term used to describe a class of machine learning algorithms that rely on transient dynamics of a dynamical system to implement and manipulate goal-related information.

The most famous example is echo-state networks, which uses random recurrent neural networks as reservoirs, but other dynamical systems can also be used.

## Reservoir computing with cellular automata

Reservoir computing can use cellular automata as the reservoir. Some citations (Nichele, Molund 2017; Yilmaz 2014; Morán et al. 2018; Babson et al. 2019).

## Echo-state networks

## Reservoir computing for differential equation solving

## Reservoir computing in physical media

## Bibliography

- Stefano Nichele, Andreas Molund. . "Deep Reservoir Computing Using Cellular Automata".
*Arxiv:1703.02806 [cs]*. http://arxiv.org/abs/1703.02806. - Ozgür Yilmaz. . "Reservoir Computing Using Cellular Automata".
*Arxiv:1410.0162 [cs]*. http://arxiv.org/abs/1410.0162. - Alejandro Morán, Christiam F. Frasser, Josep L. Rosselló. . "Reservoir Computing Hardware with Cellular Automata".
*Arxiv:1806.04932 [nlin]*. http://arxiv.org/abs/1806.04932. - Neil Babson, Christof Teuscher, Portland State University. . "Reservoir Computing with Complex Cellular Automata".
*Complex Systems*28 (4):433–55. DOI. - Marios Mattheakis, Hayden Joy, Pavlos Protopapas. . "Unsupervised Reservoir Computing for Solving Ordinary Differential Equations".
*Arxiv:2108.11417 [physics]*. http://arxiv.org/abs/2108.11417. - Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, Akira Hirose. . "Recent Advances in Physical Reservoir Computing: A Review".
*Neural Networks*115 (July):100–123. DOI.