An Introduction to Reservoir Computing
Abstract
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.
Fields of Study & Subjects
Neural Networks and Reservoir Computing
Neural Networks and Applications
Publication details
- Journal / source
- Machine intelligence for materials science
- Publisher
- Springer Nature Switzerland
- Pages
- 53–66
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Book chapter