An independent coding scheme for idiothetic distance in the hippocampus
Bibliographic record
Abstract
Self-location and navigation are crucial to daily life, and their disruption is particularly disabling. The hippocampus is central to this process as well as episodic memory. Hippocampal place cells are spatially selective, increasing their activity in specific locations. When external landmarks are scarce, place cells use self-motion (idiothetic) information to track location based on distance traveled (distance coding). Distance coding may be particularly relevant to episodic memory. However, the determinants of distance coding are poorly understood. Here, we used virtual reality, electrophysiological recordings in mice, and local cue manipulations to isolate and characterize hippocampal distance coding. In cue-poor conditions, a global distance coding scheme dominated hippocampal activity with high distance indices in all place cells, including both superficial and deep CA1 pyramidal cells. The mapping of distance onto a low-dimensional manifold and rigid distance relationships between place fields are compatible with attractor dynamics similar to those observed for grid cells. Inactivation of the medial septum (MS), which disrupts grid cells, significantly reduced both distance coding and rigid distance dynamics, suggesting an alteration (but not complete abolition) of the underlying attractor. By contrast, place cell coding was also influenced by local visual cues in cue-rich environments, notably deep CA1 pyramidal cells, and this persisted under MS inactivation. We propose that grid cells and associated rigid attractor dynamics predominantly contribute to hippocampal distance coding.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".