Semicircular Canal Afferents Similarly Encode Active and Passive Head-On-Body Rotations: Implications for the Role of Vestibular Efference
Bibliographic record
Abstract
The vestibular receptors in the labyrinth receive innervation from centrifugally projecting efferent fibers. The influence of these efferents on information processing by vestibular afferents in primates has not been determined. One commonly held notion is that efferent activation during large-amplitude, active head movements would result in an increase in the resting discharge rate and in a reduction of the rotational sensitivity of afferents. Such an effect would increase the dynamic range of afferents involved in the encoding of head movements. To test this hypothesis, we recorded from afferents innervating the semicircular canals in alert macaques during passive head-on-body rotations and during active head movements that included gaze shifts and gaze pursuit. Extracellular, single-unit recordings were obtained from 24 afferent fibers innervating the horizontal, superior, and posterior canals. Based on the normalized coefficient of variation of the interspike interval for these units, our sample contained six regularly discharging, six intermediate, and 12 irregularly discharging afferents. Responses were analyzed using a least squares regression to determine the bias discharge rate of each unit and sensitivity to head velocity and acceleration. We found no difference in bias discharge rate or rotational sensitivity of the afferent responses for the different stimulus conditions tested. Our results indicate that semicircular canal afferents encode information about head rotation similarly for self generated and passively applied head-on-body movements.
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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 | 0.000 |
| 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".