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.