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Connecting reservoir computing with statistical forecasting and deep neural networks

Author

Listed:
  • Lina Jaurigue

    (Institut für Theoretische Physik)

  • Kathy Lüdge

    (Institut für Physik)

Abstract

Standfirst Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.

Suggested Citation

  • Lina Jaurigue & Kathy Lüdge, 2022. "Connecting reservoir computing with statistical forecasting and deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-3, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-021-27715-5
    DOI: 10.1038/s41467-021-27715-5
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    References listed on IDEAS

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    1. Florian Stelzer & André Röhm & Raul Vicente & Ingo Fischer & Serhiy Yanchuk, 2021. "Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. Daniel J. Gauthier & Erik Bollt & Aaron Griffith & Wendson A. S. Barbosa, 2021. "Next generation reservoir computing," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    3. L. Appeltant & M.C. Soriano & G. Van der Sande & J. Danckaert & S. Massar & J. Dambre & B. Schrauwen & C.R. Mirasso & I. Fischer, 2011. "Information processing using a single dynamical node as complex system," Nature Communications, Nature, vol. 2(1), pages 1-6, September.
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