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arXiv · 2504.12695

Attractor-merging Crises and Intermittency in Reservoir Computing

Tempei Kabayama·Motomasa Komuro·Yasuo Kuniyoshi·Kazuyuki Aihara·Kohei Nakajima
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Abstract

Reservoir computing can embed attractors into random neural networks (RNNs), generating a ``mirror'' of a target attractor because of its inherent symmetrical constraints. In these RNNs, we report that an attractor-merging crisis accompanied by intermittency emerges simply by adjusting the global parameter. We further reveal its underlying mechanism through a detailed analysis of the phase-space structure and demonstrate that this bifurcation scenario is intrinsic to a general class of RNNs, independent of training data.

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Tempei Kabayama, Motomasa Komuro, Yasuo Kuniyoshi, Kazuyuki Aihara, Kohei Nakajima. 2025-04-17. Attractor-merging Crises and Intermittency in Reservoir Computing. https://doi.org/10.1103/vqvp-mbxx

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