ArticleProceedings of the National Academy of Sciences of the United States of America2024
Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Conditions for replay of neuronal assemblies.PLoS computational biology · 2026Article
- Learning neural dynamics through instructive signals.bioRxiv : the preprint server for biology · 2025Article
- Inferring neural population codes forProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cognitive network interactions through communication subspaces in large-scale models of the neocortex.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
Understanding how neural circuits generate sequential activity is a longstanding challenge. While foundational theoretical models have shown how sequences can be stored as memories in neural networks with Hebbian plasticity rules, these models considered only a narrow range of Hebbian rules. Here, we introduce a model for arbitrary Hebbian plasticity rules, capturing the diversity of spike-timing-dependent synaptic plasticity seen in experiments, and show how the choice of these rules and of neural activity patterns influences sequence memory formation and retrieval. In particular, we derive a general theory that predicts the tempo of sequence replay. This theory lays a foundation for explaining how cortical tutor signals might give rise to motor actions that eventually become "automatic." Our theory also captures the impact of changing the tempo of the tutor signal. Beyond shedding light on biological circuits, this theory has relevance in artificial intelligence by laying a foundation for frameworks whereby slow and computationally expensive deliberation can be stored as memories and eventually replaced by inexpensive recall.
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Registered trials
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