ArticleScience advances2025
A non-Hebbian code for episodic memory.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Synchronization-Driven Learning Rule for Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.Research square · 2026Article
- Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
2 authors.
Funding
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Abstract
Hebbian plasticity has long dominated neurobiological models of memory formation. Yet, plasticity rules operating on one-shot episodic memory timescales rarely depend on both pre- and postsynaptic spiking, challenging Hebbian theory in this crucial regime. Here, we present an episodic memory model governed by a simpler rule depending only on presynaptic activity. We show that this rule, capitalizing on high-dimensional neural activity with restricted transitions, naturally stores episodes as paths through complex state spaces like those underlying a world model. The resulting memory traces, which we term path vectors, are highly expressive and decodable with an odor-tracking algorithm. We show that path vectors are robust alternatives to Hebbian traces, support one-shot sequential and associative recall, along with policy learning, and shed light on specific hippocampal plasticity rules. Thus, non-Hebbian plasticity is sufficient for flexible memory and learning and well-suited to encode episodes and policies as paths through a world model.
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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.