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Crisis-induced intermittency in reservoir computing

Tempei Kabayama1,*, Motomasa Komuro2, Yasuo Kuniyoshi1, Kazuyuki Aihara2, and Kohei Nakajima1,2

  • 1Graduate School of Information Science and Technology, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
  • 2International Research Center for Neurointelligence, University of Tokyo Institutes for Advanced Study, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan

  • *Contact author: t-kabayama@isi.imi.i.u-tokyo.ac.jp

From chaotic itinerancy to intermittent synchronization in complex networks

I. Leyva, Irene Sendiña-Nadal, R. Sevilla-Escoboza, V. P. Vera-Ávila, and Christophe Letellier

Phys. Rev. Research 8 013328 (2026)

Two-shot learning of multiple strange attractors

Daniel Köglmayr, Miralem Spahic, Andrew Flynn, and Christoph Räth

Neural Networks 204 109209 (2026)

Reservoir computing bootcamp—From Python/NumPy tutorial for the complete beginners to cutting-edge research topics of reservoir computing

Katsuma Inoue, Tomoyuki Kubota, Quoc Hoan Tran, Nozomi Akashi, Ryo Terajima, Tempei Kabayama, JingChuan Guan, and Kohei Nakajima

Chaos: An Interdisciplinary Journal of Nonlinear Science 36 023109 (2026)

Pseudo-Lorenz attractors in echo state networks

Tempei Kabayama, Motomasa Komuro, Kazuyuki Aihara, and Kohei Nakajima

Japan J. Indust. Appl. Math. 42 1459 (2025)

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