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.

Imagine if the pullover you're wearing automatically adapted itself to the temperature, warming you if you were shivering, or cooling you down if you were sweating. This means that the pullover would have to learn to recognize your discomfort and alter its properties so as to counter this discomfort. Other potential functionalities could include rapid drying or cushioning a fall. But how can such a pullover be created? What energy would it need?