ArticleNature communications2024
Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure.
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.
- GATE: Adaptive learning with working memory by information gating in multi-lamellar hippocampal formation.PLoS computational biology · 2026Article
- Article
- Contextual inference through flexible integration of environmental features and behavioural outcomes.PLoS computational biology · 2026Article
- Ozone-induced cognitive deficits are mediated by the liver-brain axis: peripheral complement C3 triggers microglial synaptic phagocytosis.Journal of neuroinflammation · 2026Article
- The relevance of context in memory tasks influences the magnitude of hippocampal remapping.Cell reports · 2025Article
- Behavioral Timescale Synaptic Plasticity: A Burst in the Field of Learning and Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Review
- The medial septum-hippocampus-lateral septum circuitry in spatial memory: linking healthy function to early Alzheimer's disease and translational opportunities.Translational neurodegeneration · 2025Review
- Article
- Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure.Nature communications · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
To successfully learn real-life behavioral tasks, animals must pair actions or decisions to the task's complex structure, which can depend on abstract combinations of sensory stimuli and internal logic. The hippocampus is known to develop representations of this complex structure, forming a so-called "cognitive map". However, the precise biophysical mechanisms driving the emergence of task-relevant maps at the population level remain unclear. We propose a model in which plateau-based learning at the single cell level, combined with reinforcement learning in an agent, leads to latent representational structures codependently evolving with behavior in a task-specific manner. In agreement with recent experimental data, we show that the model successfully develops latent structures essential for task-solving (cue-dependent "splitters") while excluding irrelevant ones. Finally, our model makes testable predictions concerning the co-dependent interactions between split representations and split behavioral policy during their evolution.
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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.