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Reservoir Computing Trends

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Abstract

Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the readouts from it. It started ten years ago and is currently a prolific research area, giving important insights into RNNs, practical machine learning tools, as well as enabling computation with non-conventional hardware. Here we give a brief introduction into basic concepts, methods, insights, current developments, and highlight some applications of RC.

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Acknowledgements

The authors acknowledge support by the European FP7 project ORGANIC (http://reservoir-computing.org/organic). Patent note. The basic ESN architecture and algorithm are protected for commercial use by international patents held by the Fraunhofer Society [18].

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Authors and Affiliations

  1. Jacobs University Bremen, Campus Ring 1, 28759, Bremen, Germany

    Mantas Lukoševičius & Herbert Jaeger

  2. Ghent University, Sint Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Benjamin Schrauwen

Authors
  1. Mantas Lukoševičius
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  2. Herbert Jaeger
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  3. Benjamin Schrauwen
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Correspondence to Mantas Lukoševičius.

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Lukoševičius, M., Jaeger, H. & Schrauwen, B. Reservoir Computing Trends. Künstl Intell 26, 365–371 (2012). https://doi.org/10.1007/s13218-012-0204-5

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