Navigational Frames of Reference as Critical Regulators of Hippocampal Interneuron Coding Properties
Bibliographic record
Abstract
Abstract Efficient spatial navigation relies on the hippocampus integrating local (proximal) and global (distal) cues, collectively called frames of reference, to guide behavior and support memory. Although these cues control the anchoring of principal cell fields, how these frames tune interneuron functions remains unknown. Traditionally, interneurons such as O-LM and VIP cells have been viewed primarily as speed encoders, although some also encode spatial information or respond to discrete stimuli. Using calcium imaging in freely behaving mice performing a new spatial learning task that differentiates between reference frames, we demonstrate that O-LM cells displayed a striking bimodal activity pattern, altering both their speed and spatial encoding properties. In contrast, VIP interneurons were largely unaffected by changes in the frame of reference, instead correlating with familiarization. Notably, linear decoding using speed scores revealed that only O-LM interneurons provide an accurate readout of the dominant reference frame, enabling prediction of the animal’s navigation strategy. These findings highlight that hippocampal interneurons can flexibly adapt their functions depending on cognitive factors such as the reference frames used to guide behavior.
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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 | 0.001 | 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".