Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into computational spaces through a fixed, nonlinear system called a reservoir.
In machine learning, feed-forward structures, such as artificial neural networksartificial neural networks, graphical Bayesian models, and kernel methods, have been studied for the processing of non-temporal problems. These methods are well understood due to their non-dynamic nature. The feed-forward network is a fundamental building block of a neural network. However, as the appeal of neural networks is the possibility of parallel with the human brain, the network architecture of which is not a feedforward, and this understanding lead to the recurrent neural networks. In 2001, with difficulties in developing recurrent neural networks, a new approach to design and training was proposed independently by Wolfgang Maass and Herbert Jaeger. These respective approaches were called Liquid State Machines and Echo State Networks.