Responses to cochlear normalized speech stimuli in the auditory nerve of cat
Bibliographic record
Abstract
Previous studies of auditory-nerve fiber (ANF) representation of vowels in cats and rodents (chinchillas and guinea pigs) have shown that, at amplitudes typical for conversational speech (60-70 dB), neuronal firing rate as a function of characteristic frequency alone provides a poor representation of spectral prominences (e.g., formants) of speech sounds. However, ANF rate representations may not be as inadequate as they appear. Here, it is investigated whether some of this apparent inadequacy owes to the mismatch between animal and human cochlear characteristics. For all animal models tested in earlier studies, the basilar membrane is shorter and encompasses a broader range of frequencies than that of humans. In this study, a customized speech synthesizer was used to create a rendition of the vowel [E] with formant spacing and bandwidths that fit the cat cochlea in proportion to the human cochlea. In these vowels, the spectral envelope is matched to cochlear distance rather than to frequency. Recordings of responses to this cochlear normalized [E] in auditory-nerve fibers of cats demonstrate that rate-based encoding of vowel sounds is capable of distinguishing spectral prominences even at 70-80-dB SPL. When cochlear dimensions are taken into account, rate encoding in ANF appears more informative than was previously believed.
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 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".