Gain adaptation and variability of vestibular corticothalamic neurons shape our perception of natural self motion stimuli
Bibliographic record
Abstract
Natural stimuli display complex spatiotemporal characteristics. In order to encode such stimuli efficiently, sensory systems must continuously adapt by changing their response properties. The computational role of such adaptation remains poorly understood because adaptation can increase coding ambiguity. We investigated how vestibular thalamocortical neurons (VTN) and their afferent input within the vestibular nuclei (VON) respond to simple artificial and complex natural selfmotion stimuli in rhesus macaques. We found that both groups displayed comparable response properties to artificial stimuli which led to ambiguity. While such ambiguity persisted for artificial stimuli for VON, VTN instead faithfully followed the timecourse of natural selfmotion stimuli. A model including gain adaptation successfully reproduced our experimental data. Our results challenge the common wisdom that adaptation leads to ambiguity by showing that such adaptation actually leads to unambiguous encoding of natural stimuli. Second, we investigated the role of these VTN in the perception of selfmotion. We tested whether their responses can account for violation of Weber's law, i.e. discrimination performance is enhanced at higher stimulus amplitudes. While neural gain decreased as a function of stimulus amplitude, neural variability saturated at high values. As a result, neural populations thresholds saturated and agreed with perception. Taken together, we provide novel insights as to how variability and gain control contribute to encoding of natural stimuli with continually varying statistics.
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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".