Reservoir computing is a computational framework that leverages the dynamic behavior of recurrent neural networks, where a fixed, random network (the reservoir) processes input data to generate rich dynamics. This approach simplifies training by only adjusting the output layer while keeping the reservoir's connections unchanged, making it particularly suited for tasks involving temporal data and complex dynamical systems.
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