Evidence map›Paper›PMID 31408264›Full record

ArticleHippocampus2020

Navigating with grid and place cells in cluttered environments.

Vegard Edvardsen, Andrej Bicanski, Neil Burgess

Abstract read
In one paragraph

Article in Hippocampus, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

  1. Cognitive maps as a medium for thought.Trends in cognitive sciences · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Expert navigators deploy rational complexity-based decision precaching for large-scale real-world planning.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Review
  8. Article
  9. Learning dynamic cognitive map with autonomous navigation.Frontiers in computational neuroscience · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. A unified energy-optimality criterion predicts human navigation paths and speeds.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Vegard EdvardsenDepartment of Computer Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0002-3357-9544
Andrej BicanskiInstitute of Cognitive Neuroscience, University College London, Alexandra House, 17 Queen Square, WC1N 3AZ London, UK.
Neil BurgessInstitute of Cognitive Neuroscience, University College London, Alexandra House, 17 Queen Square, WC1N 3AZ London, UK.

Funding

Wellcome Trust 202805/Z/16/Z
6 · The paper itself

Abstract

Hippocampal formation contains several classes of neurons thought to be involved in navigational processes, in particular place cells and grid cells. Place cells have been associated with a topological strategy for navigation, while grid cells have been suggested to support metric vector navigation. Grid cell-based vector navigation can support novel shortcuts across unexplored territory by providing the direction toward the goal. However, this strategy is insufficient in natural environments cluttered with obstacles. Here, we show how navigation in complex environments can be supported by integrating a grid cell-based vector navigation mechanism with local obstacle avoidance mediated by border cells and place cells whose interconnections form an experience-dependent topological graph of the environment. When vector navigation and object avoidance fail (i.e., the agent gets stuck), place cell replay events set closer subgoals for vector navigation. We demonstrate that this combined navigation model can successfully traverse environments cluttered by obstacles and is particularly useful where the environment is underexplored. Finally, we show that the model enables the simulated agent to successfully navigate experimental maze environments from the animal literature on cognitive mapping. The proposed model is sufficiently flexible to support navigation in different environments, and may inform the design of experiments to relate different navigational abilities to place, grid, and border cell firing.

Indexed as

Models, NeurologicalAnimalsEntorhinal CortexEnvironmentGrid CellsHippocampusPlace CellsSpatial Navigationentorhinal cortexgrid cellshippocampusplace cellsspatial navigation

Identifiers

PMID31408264
PMCPMC8641373

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.