ArticleeLife2022
Sampling motion trajectories during hippocampal theta sequences.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Neural sampling from cognitive maps enables goal-directed imagination and planning.Nature machine intelligence · 2026Article
- Article
- Article
- Chaotic neural dynamics facilitate probabilistic computations through sampling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Distinguishing examples while building concepts in hippocampal and artificial networks.Nature communications · 2024Article
- Review
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Authors and funding
2 authors.
Funding
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Abstract
Efficient planning in complex environments requires that uncertainty associated with current inferences and possible consequences of forthcoming actions is represented. Representation of uncertainty has been established in sensory systems during simple perceptual decision making tasks but it remains unclear if complex cognitive computations such as planning and navigation are also supported by probabilistic neural representations. Here, we capitalized on gradually changing uncertainty along planned motion trajectories during hippocampal theta sequences to capture signatures of uncertainty representation in population responses. In contrast with prominent theories, we found no evidence of encoding parameters of probability distributions in the momentary population activity recorded in an open-field navigation task in rats. Instead, uncertainty was encoded sequentially by sampling motion trajectories randomly and efficiently in subsequent theta cycles from the distribution of potential trajectories. Our analysis is the first to demonstrate that the hippocampus is well equipped to contribute to optimal planning by representing uncertainty.
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Registered trials
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