Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Recurrent Network Models, Reservoir Computing

  • Robert LegensteinEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_796-1



Reservoir computing (RC) is a general concept for computation and learning on temporal input streams with dynamical systems. Time can either be continuous or discrete. We will here use continuous time for the general definition. An input-driven dynamical system (the reservoir) provides a nonlinearly transformed and temporally integrated representation of the input stream in terms of its internal state. This representation is utilized by a readoutwhich maps internal states to outputs of the system. The output of the readout can be fed back into the reservoir. The reservoir is typically a recurrent neural network, but other dynamical systems have been employed in the RC spirit recently. The readout function is adapted through some learning procedure. Originally, this training was supervised and the reservoir was not adapted. Today, many...


Recurrent Neural Network Input Stream Reservoir State Recurrent Network Echo State Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute for Theoretical Computer ScienceGraz University of TechnologyGrazAustria