ArticleeLife2023
A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Dynamic Hippocampal-Striatal Information Flow Accompanies Behavioral Strategy Transitions During Sequential Learning in Pigeons: A Preliminary Study.Animals : an open access journal from MDPI · 2026Article
- Prefrontal-thalamic goal states organize spatially aligned hippocampal maps.Nature communications · 2026Article
- Accounting for sensitivity of latent learning to behavioral statistics with successor representations.PLoS computational biology · 2026Article
- Article
- Behavioral Timing of Interictal Spikes, But Not Rate, Correlates with Impaired Working Memory Performance.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Interictal spikes during spatial working memory carry helpful or distracting representations of space and have opposing impacts on performance.bioRxiv : the preprint server for biology · 2024Article
- Distinct mechanisms and functions of episodic memory.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Coordinated NREM sleep oscillations among hippocampal subfields modulate synaptic plasticity in humans.Communications biology · 2024Article
- Navigation and the efficiency of spatial coding: insights from closed-loop simulations.Brain structure & function · 2024Article
- Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model.Frontiers in computational neuroscience · 2024Article
- Modeling the function of episodic memory in spatial learning.Frontiers in psychology · 2023Article
- CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning.Frontiers in neuroinformatics · 2023Article
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2 authors.
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Abstract
Replay of neuronal sequences in the hippocampus during resting states and sleep play an important role in learning and memory consolidation. Consistent with these functions, replay sequences have been shown to obey current spatial constraints. Nevertheless, replay does not necessarily reflect previous behavior and can construct never-experienced sequences. Here, we propose a stochastic replay mechanism that prioritizes experiences based on three variables: 1. Experience strength, 2. experience similarity, and 3. inhibition of return. Using this prioritized replay mechanism to train reinforcement learning agents leads to far better performance than using random replay. Its performance is close to the state-of-the-art, but computationally intensive, algorithm by Mattar & Daw (2018). Importantly, our model reproduces diverse types of replay because of the stochasticity of the replay mechanism and experience-dependent differences between the three variables. In conclusion, a unified replay mechanism generates diverse replay statistics and is efficient in driving spatial learning.
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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.