Modeling of auditory nerve fiber input/output functions near threshold
Bibliographic record
Abstract
The instantaneous discharge rate of auditory nerve fibers (ANFs) near their threshold exhibits an approximately exponential relationship to the instantaneous pressure at the eardrum after compensating for the discharge latency. Some simplified models of the pressure-to-discharge transduction process in the cochlea have attributed this exponential relationship to an inner hair cell (IHC) transduction current that is either an exponential function with a level-dependent slope parameter or a first-order Boltzmann function (which is approximately exponential around the zero-input point) followed by a low-pass filter. However, such IHC transduction functions do not produce level-dependent changes in the AC and DC components of the IHC potential that match the behavior observed in physiological recordings. In this study, we show that retaining the physiologically-accurate IHC transduction model of Bruce et al. (Hear. Res., 2018) and following it by an exponential or exponential-like function that maps the IHC potential to the input of the synaptic power-law adaptation in that model produces the desired exponential input/output behavior near threshold while preserving the appropriate level-dependent changes in ANF discharge rate and phase-locking. [Work supported by NSERC Discovery Grant #RGPIN-2018-05778.]
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".