# An introduction to reservoir computing

arXiv: https://arxiv.org/abs/2412.13212 · HTML: https://mlsanity.com/paper/2412.13212/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 56 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Do Reservoir Computers Work Best at the Edge of Chaos?](https://mlsanity.com/paper/2012.01409/) (2020)
- [Biological neurons act as generalization filters in reservoir computing](https://mlsanity.com/paper/2210.02913/) (2022)
- [Exploiting short-term memory in soft body dynamics as a computational resource](https://mlsanity.com/paper/1406.4929/) (2014)
- [DNA Reservoir Computing: A Novel Molecular Computing Approach](https://mlsanity.com/paper/1306.5998/) (2013)

## Built on by (most cited first, 2 of 2)

- [From Perception to Action: Spatial AI Agents and World Models](https://mlsanity.com/paper/2602.01644/) (2026)
- [Quantum Reservoir Computing: Recent Advances and Future Directions](https://mlsanity.com/paper/2607.18552/) (2026)

## Abstract (arXiv)

There is a growing interest in the development of artificial neural networks that are implemented in a physical system. A major challenge in this context is that these networks are difficult to train since training here would require a change of physical parameters rather than simply of coefficients in a computer program. For this reason, reservoir computing, where one employs high-dimensional recurrent networks and trains only the final layer, is widely used in this context. In this chapter, I introduce the basic concepts of reservoir computing. Moreover, I present some important physical implementations coming from electronics, photonics, spintronics, mechanics, and biology. Finally, I provide a brief discussion of quantum reservoir computing.