Attractor-merging Crises and Intermittency in Reservoir Computing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916955896676352 |
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| author | Kabayama, Tempei Komuro, Motomasa Kuniyoshi, Yasuo Aihara, Kazuyuki Nakajima, Kohei |
| author_facet | Kabayama, Tempei Komuro, Motomasa Kuniyoshi, Yasuo Aihara, Kazuyuki Nakajima, Kohei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_12695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attractor-merging Crises and Intermittency in Reservoir Computing Kabayama, Tempei Komuro, Motomasa Kuniyoshi, Yasuo Aihara, Kazuyuki Nakajima, Kohei Chaotic Dynamics Machine Learning Neural and Evolutionary Computing Dynamical Systems 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. |
| title | Attractor-merging Crises and Intermittency in Reservoir Computing |
| topic | Chaotic Dynamics Machine Learning Neural and Evolutionary Computing Dynamical Systems |
| url | https://arxiv.org/abs/2504.12695 |