Reference frames for encoding of translation and tilt in the caudal cerebellar vermis
Bibliographic record
Abstract
Abstract Many daily behaviors rely critically on estimates of our body’s motion and orientation in space. Vestibular signals are essential for such estimates but to contribute appropriately two key sets of computations are required. First, ambiguous motion information from the otolith organs must be combined with spatially transformed rotational signals (e.g., from the canals) to distinguish head translation from tilt. Second, tilt and translation estimates must be transformed from a head- to a body-centered reference frame to correctly interpret the body’s motion. Studies have shown that cells in the caudal cerebellar vermis (nodulus and ventral uvula, NU) reflect the output of the first set of computations to estimate translation and tilt. However, it remains unknown whether these estimates are encoded exclusively in head-centered coordinates or whether they reflect a further transformation towards body-centered coordinates. Here we addressed this question by examining how the 3D spatial tuning of otolith and canal signals on translation- and tilt-selective NU neurons varies with changes in head-re-body and body-re-gravity orientation. We show that NU cell tuning properties are consistent with head-centered coding of otolith signals during translation. Furthermore, while canals signals in the NU have been transformed into world-referenced estimates of reorientation relative to gravity (i.e., tilt), as needed to resolve the tilt-translation ambiguity, the resulting tilt estimates are encoded in head-centered coordinates. Our results thus suggest that body-centered motion and orientation estimates required for postural control, navigation and reaching are computed elsewhere either by further transforming NU outputs or via computations in other parallel pathways.
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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 | 0.001 |
| 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".