ArticleNature communications2021
Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 108 citations in OpenAlex.
- Inferential planning in the frontal cortex.Cell reports · 2026Article
- Planning in the Brain: It's Not What You Think It Is.Annual review of neuroscience · 2026Review
- GATE: Adaptive learning with working memory by information gating in multi-lamellar hippocampal formation.PLoS computational biology · 2026Article
- The hippocampus as a small-world cognitive map.bioRxiv : the preprint server for biology · 2026Article
- Article
- Action information is integrated into entorhinal representations of conceptual space and is reflected in eye movements.PLoS biology · 2026Article
- Building Goal-Directed Cognitive Graphs.bioRxiv : the preprint server for biology · 2026Article
- Composing egocentric and allocentric maps for flexible navigation.PLoS computational biology · 2026Article
- Neural sampling from cognitive maps enables goal-directed imagination and planning.Nature machine intelligence · 2026Article
- Episodic-like memory in a simulation of cuttlefish behavior.Scientific reports · 2025Article
- The Primate Hippocampus Constructs a Temporal Scaffold Anchored to Behavioral Events.bioRxiv : the preprint server for biology · 2025Article
- Stability through plasticity: Finding robust memories through representational drift.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Hippocampus supports multi-task reinforcement learning under partial observability.Nature communications · 2025Article
- Episodic-Like Memory in a Simulation of Cuttlefish Behavior.bioRxiv : the preprint server for biology · 2025Article
- Reconciling flexibility and efficiency: medial entorhinal cortex represents a compositional cognitive map.Nature communications · 2025Article
- Learning predictive signals within a local recurrent circuit.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Constructing future behavior in the hippocampal formation through composition and replay.Nature neuroscience · 2025Article
- Article
- A unified neural representation model for spatial and conceptual computations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A detailed theory of thalamic and cortical microcircuits for predictive visual inference.Science advances · 2025Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization and efficient planning. Here we propose a specific higher-order graph structure, clone-structured cognitive graph (CSCG), which forms clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety of cognitive map phenomena such as discovering spatial relations from aliased sensations, transitive inference between disjoint episodes, and formation of transferable schemas. Learning different clones for different contexts explains the emergence of splitter cells observed in maze navigation and event-specific responses in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for disparate place cell remapping phenomena. By lifting aliased observations into a hidden space, CSCGs reveal latent modularity useful for hierarchical abstraction and planning. Altogether, CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.
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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.