Anchoring of grid fields selectively enhances localisation by path integration
Bibliographic record
Abstract
Abstract Grid firing fields of neurons in the medial entorhinal cortex have been proposed as a neural substrate for spatial localisation and path integration. While there are strong theoretical arguments to support these roles, it has been challenging to directly test whether and when grid cells contribute to behaviours. Here, we investigate firing of grid cells during a task in which mice obtain rewards by recalling a location on a linear virtual track. We find that grid firing can either be anchored to the track, providing a code for position, or can instead encode distance travelled independent from the track position. Because engagement of these representations varied between and within sessions we were able to test whether positional grid firing predicts behaviour. We find that when a visual cue indicates the reward location, performance is similar regardless of whether grid cells encode position or distance. By contrast, in the absence of the visual cue, performance was substantially improved when grid cells encoded position compared to when they encoded distance. Our results suggest that positional anchoring of grid firing enhances performance of tasks that require path integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".