ArticleNature communications2024
Reservoir-computing based associative memory and itinerancy for complex dynamical attractors.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Tailored forecasting from short time series via meta-learning.Science advances · 2026Article
- Limit-Cycle Proliferation Under Parametric Delayed Feedback in a Conductance-Based Neuron: Bifurcation Landscape, Orbit Catalog, and Capacity Analysis.Entropy (Basel, Switzerland) · 2026Article
- Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Very-large-scale mimetic optogenetic synapses for physical reservoir computing.Nature communications · 2026Article
- Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations.Nature communications · 2025Article
- How more data can hurt: Instability and regularization in next-generation reservoir computing.Chaos (Woodbury, N.Y.) · 2025Article
- A Neuransistor with Excitatory and Inhibitory Neuronal Behaviors for Liquid State Machine.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Predicting three-dimensional chaotic systems with four qubit quantum systems.Scientific reports · 2025Article
- Reservoir-computing based associative memory and itinerancy for complex dynamical attractors.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.