An introduction to reservoir computing

• URL: http://arxiv.org/abs/2412.13212v1
• Date: Thu, 12 Dec 2024 21:19:52 GMT
• Title: An introduction to reservoir computing
• Authors: Michael te Vrugt,
• Abstract summary: Reservoir computing employs high-dimensional recurrent networks and trains only the final layer.<n>I present some important physical implementations coming from electronics, photonics, spintronics, mechanics, and biology.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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