DISTINCT NEURAL SIGNATURES OF HIPPOCAMPAL POPULATION DYNAMICS DURING LOCOMOTION-IN-PLACE
Bibliographic record
Abstract
Hippocampal CA1 neurons modulate their activity with movement variables such as time, distance, and speed, yet it remains unclear how these representations reorganize across behavioral states, from externally driven to self-paced movement and immobility. Here, we investigated how sensory events that initiate or terminate locomotion, structure CA1 population codes and how these codes reorganize across sensory-driven locomotion, spontaneous locomotion, and forced immobility. Using two-photon calcium imaging in head-fixed Thy1-GCaMP mice (n = 5) performing the air-induced running task on a non-motorized conveyor belt, we examined neuronal firing-rate modulation across a series of behavioral configurations designed to probe distinct forms of locomotion-in-place. In the No-Brake (locomotion-permitted) condition, the belt rotated freely, allowing animals to execute full cyclic limb movements in response to air stimulation. In the Brake (immobility) condition, the belt was fixed, restricting movement to partial or attempted locomotor motions. Firing-rate modulation with respect to time, distance, and speed was quantified using linear (Pearson correlation) and nonlinear (mutual information) metrics under permutation testing in the natural reference domains. Behaviorally, air stimuli produced faster, sustained running during air-on and more variable, self-paced movement during air-off. Neurally, a larger fraction of CA1 cells was active and significantly modulated during air-off. Within the modulated set, singularly tuned cells (time, distance, or speed) predominated over mixed-tuned cells, and speed-modulated cells peaked earlier after stimulus onset or offset than time- or distance-modulated cells. Under Brake, CA1 activity was predominantly singularly tuned to time or movement-in-place, with stronger movement modulation and engagement post-stimulation. Despite substantial single-cell turnover across configurations and phases, population-level analyses revealed a coherent, air-phase-locked organization and distinct movement-related populations across Brake and No-Brake conditions. These results indicate a state-dependent reweighting of sensorimotor features implemented atop a conserved ensemble scaffold in CA1.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".