Representation of the vowel /ε/ in normal and impaired auditory nerve fibers: Model predictions of responses in cats
Bibliographic record
Abstract
The temporal response of auditory-nerve (AN) fibers to a steady-state vowel is investigated using a computational auditory-periphery model. The model predictions are validated against a wide range of physiological data for both normal and impaired fibers in cats. The model incorporates two parallel filter paths, component 1 (C1) and component 2 (C2), which correspond to the active and passive modes of basilar membrane vibration, respectively, in the cochlea. The outputs of the two filters are subsequently transduced by two separate functions, added together, and then low-pass filtered by the inner hair cell (IHC) membrane, which is followed by the IHC-AN synapse and discharge generator. The C1 response dominates at low and moderate levels and is responsible for synchrony capture and multiformant responses seen in the vowel responses. The C2 response dominates at high levels and contributes to the loss of synchrony capture observed in normal and impaired fibers. The interaction between C1 and C2 responses explains the behavior of AN fibers in the transition region, which is characterized by two important observations in the vowel responses: First, all components of the vowel undergo the C1/C2 transition simultaneously, and second, the responses to the nonformant components of the vowel become substantial.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".