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Reservoir computing based on quenched chaos

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  • Choi, Jaesung
  • Kim, Pilwon

Abstract

Reservoir computing (RC) is a brain-inspired computing framework that employs a transient dynamical system whose reaction to an input signal is transformed to a target output. One of the central problems in RC is to find a reliable reservoir with a large criticality, since computing performance of a reservoir is maximized near the phase transition. In this work, we propose a continuous reservoir that utilizes transient dynamics of coupled chaotic oscillators in a critical regime where sudden amplitude death occurs. This “explosive death” not only brings the system a large criticality which provides a variety of orbits for computing, but also stabilizes them which otherwise diverge soon in chaotic units. The proposed framework shows better results in tasks for signal reconstructions than RC based on explosive synchronization of regular phase oscillators. We also show that the information capacity of the reservoirs can be used as a predictive measure for computational capability of a reservoir at a critical point.

Suggested Citation

  • Choi, Jaesung & Kim, Pilwon, 2020. "Reservoir computing based on quenched chaos," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305270
    DOI: 10.1016/j.chaos.2020.110131
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    References listed on IDEAS

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    1. Bruno Del Papa & Viola Priesemann & Jochen Triesch, 2017. "Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-21, May.
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    Cited by:

    1. Liu, Lingfeng & Wang, Jie, 2023. "A cluster of 1D quadratic chaotic map and its applications in image encryption," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 89-114.

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