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Reservoir-computing based associative memory and itinerancy for complex dynamical attractors

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  • Ling-Wei Kong

    (Cornell University
    Arizona State University)

  • Gene A. Brewer

    (Arizona State University)

  • Ying-Cheng Lai

    (Arizona State University
    Arizona State University)

Abstract

Traditional neural network models of associative memories were used to store and retrieve static patterns. We develop reservoir-computing based memories for complex dynamical attractors, under two common recalling scenarios in neuropsychology: location-addressable with an index channel and content-addressable without such a channel. We demonstrate that, for location-addressable retrieval, a single reservoir computing machine can memorize a large number of periodic and chaotic attractors, each retrievable with a specific index value. We articulate control strategies to achieve successful switching among the attractors, unveil the mechanism behind failed switching, and uncover various scaling behaviors between the number of stored attractors and the reservoir network size. For content-addressable retrieval, we exploit multistability with cue signals, where the stored attractors coexist in the high-dimensional phase space of the reservoir network. As the length of the cue signal increases through a critical value, a high success rate can be achieved. The work provides foundational insights into developing long-term memories and itinerancy for complex dynamical patterns.

Suggested Citation

  • Ling-Wei Kong & Gene A. Brewer & Ying-Cheng Lai, 2024. "Reservoir-computing based associative memory and itinerancy for complex dynamical attractors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49190-4
    DOI: 10.1038/s41467-024-49190-4
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    References listed on IDEAS

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    4. 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.
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