Measurements and models of auditory nerve spike rate and timing to electrical stimulation
Bibliographic record
Abstract
Physiological measurements of the response of auditory nerve (AN) fibers to electrical stimulation from a cochlear implant (CI) have in general exhibited much higher maximal entrainment rates and higher temporal precision than is observed for acoustical stimulation. However, it appears that this superior rate coding in the AN for electrical stimulation does not translate to better coding of rate in the midbrain or for the percept of temporal pitch. Similarly, the enhanced temporal precision for electrical stimulation does not lead to improved coding of interaural timing differences. In this talk, I will review physiological data and computational model simulations that indicate that the superior spike rate and timing representation for CI stimulation may only hold when considering the response of some single AN fibers to repeated identical stimuli. In fact, there is a large heterogeneity in spiking responses that can change dramatically as a function of stimulus current level. A multi-compartmental computational model will be used to demonstrate how the electrode- fiber geometry and the membrane biophysics could be contributing to these phenomena. I will also discuss the implications of the likely population response of the AN to CI stimulation for brainstem processing. [Work supported by NSERC Discovery Grant 261736.]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".