Neuronal sensitivity and variability mediate 3 parallel strategies for encoding natural translations in the primate vestibular system
Bibliographic record
Abstract
Self motion is composed of both rotational and translational motions which occur with characteristic statistics in everyday life. In the context of rotation, we have previously shown that neurons at the first central stage of vestibular processing (VN) are adapted to match their tuning curves to the statistics of natural rotation. This strategy effectively removes redundancy in the neural response, and produces responses which are independent of frequency (i.e. temporally whitened). However, unlike for rotation, translation sensitive neurons display significantly more heterogeneity in their tuning curves to artificial stimuli, and their responses to natural stimuli which cover the full physiological range of motion frequencies has not been explored. Here we investigated whether and, if so, how the responses of translation sensitive vestibular-only (otolith VO) cells in the VN of awake behaving macaques are adapted to naturalistic translation stimuli. Our results indicate that otolith VOs are not consistently matched to natural statistics in the same way as their rotation-sensitive counterparts, and instead fall roughly into 3 categories covering a range from strongly low-pass filtered to optimally whitened responses. This study is the first to examine the responses of otolith VO neurons in the context of natural self-motion, and suggests that low-pass filtered, faithful reconstruction, and optimally encoded representations of the stimulus may each be implemented in the translational vestibular system via parallel encoding strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".