Multimodal Integration in Rostral Fastigial Nucleus Provides an Estimate of Body Movement
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Bibliographic record
Abstract
The ability to accurately control posture and perceive self-motion and spatial orientation requires knowledge of the motion of both the head and body. However, whereas the vestibular sensors and nuclei directly encode head motion, no sensors directly encode body motion. Instead, the convergence of vestibular and neck proprioceptive inputs during self-motion is generally believed to underlie the ability to compute body motion. Here, we provide evidence that the brain explicitly computes an internal estimate of body motion at the level of single cerebellar neurons. Neuronal responses were recorded from the rostral fastigial nucleus, the most medial of the deep cerebellar nuclei, during whole-body, body-under-head, and head-on-body rotations. We found that approximately half of the neurons encoded the motion of the body in space, whereas the other half encoded the motion of the head in space in a manner similar to neurons in the vestibular nuclei. Notably, neurons encoding body motion responded to both vestibular and proprioceptive stimulation (accordingly termed bimodal neurons). In contrast, neurons encoding head motion were sensitive only to vestibular inputs (accordingly termed unimodal neurons). Comparison of the proprioceptive and vestibular responses of bimodal neurons further revealed similar tuning in response to changes in head-on-body position. We propose that the similarity in nonlinear processing of vestibular and proprioceptive signals underlies the accurate computation of body motion. Furthermore, the same neurons that encode body motion (i.e., bimodal neurons) most likely encode vestibular signals in a body-referenced coordinate frame, since the integration of proprioceptive and vestibular information is required for both computations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it