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Reservoir computing

Reservoir computing

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.

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Amy Tomlinson Gayle
Amy Tomlinson Gayle approved a suggestion from Golden's AI on 21 Apr, 2021
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Reservoir computing differs from traditional recurrent neural network (RNN) learning techniques by making conceptual and computation separation between the reservoir and the readout. This means in contrast to traditional supervised learningsupervised learning, errors in the weights to input or in the reservoir will only influence the weights of the readout layer, as these weights are set at the start of the learning and do not change. Where in traditional supervised learning, the error between the desired output and the computed output will influence the weights of the entire network.

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