Coding Deficits in Noise-Induced Hidden Hearing Loss May Stem from Incomplete Repair of Ribbon Synapses in the Cochlea
Bibliographic record
Abstract
Recent evidence has shown that noise-induced damage to the synapse between inner hair cells (IHCs) and type I afferent auditory nerve fibers (ANFs) may occur in the absence of permanent threshold shift (PTS), and that synapses connecting IHCs with low spontaneous rate (SR) ANFs are disproportionately affected. Due to the functional importance of low-SR ANF units for temporal processing and signal coding in noisy backgrounds, deficits in cochlear coding associated with noise-induced damage may result in significant difficulties with temporal processing and hearing in noise (i.e., "hidden hearing loss"). However, significant noise-induced coding deficits have not been reported at the single unit level following the loss of low-SR units. We have found evidence to suggest that some aspects of neural coding are not significantly changed with the initial loss of low-SR ANFs, and that further coding deficits arise in association with the subsequent reestablishment of the synapses. This suggests that synaptopathy in hidden hearing loss may be the result of insufficient repair of disrupted synapses, and not simply due to the loss of low-SR units. These coding deficits include decreases in driven spike rate for intensity coding as well as several aspects of temporal coding: spike latency, peak-to-sustained spike ratio and the recovery of spike rate as a function of click-interval.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".