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Next generation reservoir computing

Author

Listed:
  • Daniel J. Gauthier

    (The Ohio State University, Department of Physics
    ResCon Technologies, LLC)

  • Erik Bollt

    (Clarkson University, Department of Electrical and Computer Engineering
    Clarkson Center for Complex Systems Science (C3S2))

  • Aaron Griffith

    (The Ohio State University, Department of Physics)

  • Wendson A. S. Barbosa

    (The Ohio State University, Department of Physics)

Abstract

Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression, which requires no random matrices, fewer metaparameters, and provides interpretable results. Here, we demonstrate that nonlinear vector autoregression excels at reservoir computing benchmark tasks and requires even shorter training data sets and training time, heralding the next generation of reservoir computing.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25801-2
    DOI: 10.1038/s41467-021-25801-2
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    Cited by:

    1. Keck, Felix & Jütte, Silke & Lenzen, Manfred & Li, Mengyu, 2022. "Assessment of two optimisation methods for renewable energy capacity expansion planning," Applied Energy, Elsevier, vol. 306(PA).
    2. Yang, J. & Primo, E. & Aleja, D. & Criado, R. & Boccaletti, S. & Alfaro-Bittner, K., 2022. "Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. Tao Wu & Xiangyun Gao & Feng An & Xiaotian Sun & Haizhong An & Zhen Su & Shraddha Gupta & Jianxi Gao & Jürgen Kurths, 2024. "Predicting multiple observations in complex systems through low-dimensional embeddings," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Ruomin Zhu & Sam Lilak & Alon Loeffler & Joseph Lizier & Adam Stieg & James Gimzewski & Zdenka Kuncic, 2023. "Online dynamical learning and sequence memory with neuromorphic nanowire networks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Berry, Christopher & Douglas Hoffman, K., 2023. "Communicating intent: Effects of employer-controlled tipping strategy disclosures on tip amount and firm evaluations," Journal of Business Research, Elsevier, vol. 160(C).
    6. Chicchi, Lorenzo & Fanelli, Duccio & Giambagli, Lorenzo & Buffoni, Lorenzo & Carletti, Timoteo, 2023. "Recurrent Spectral Network (RSN): Shaping a discrete map to reach automated classification," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    7. Zheng-Meng Zhai & Mohammadamin Moradi & Ling-Wei Kong & Bryan Glaz & Mulugeta Haile & Ying-Cheng Lai, 2023. "Model-free tracking control of complex dynamical trajectories with machine learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    8. 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.
    9. Minati, Ludovico & Bartels, Jim & Li, Chao & Frasca, Mattia & Ito, Hiroyuki, 2022. "Synchronization phenomena in dual-transistor spiking oscillators realized experimentally towards physical reservoirs," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    10. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    11. Graciela Schiliuk & Tullio Buccellato & Jens Lapointe-Rohde & Georgios Palaiodimos & Habib Attia & Marthe Memoracion Hinojales & Catharine Kho & Gennady Vasiliev & Tigran Kostanyan & Alexandra de Carv, 2021. "Regional responses to the Covid-19 crisis: a comparative study from economic, policy, and institutional perspectives," Discussion Papers 18, European Stability Mechanism, revised 08 Nov 2021.

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