Effects of an improved auditory-periphery model on the response properties of modeled neurons in the Dorsal Cochlear Nucleus
Bibliographic record
Abstract
Dorsal Cochlear Nucleus (DCN) neurons were simulated using two different models of auditory nerve (AN) fibers as inputs, the Carney model [1] and the Zilany & Bruce model [2], [3]. The change of AN models produced very different responses. DCN neurons simulated using the Zilany and Bruce model had much greater regions of excitation and significantly reduced regions of inhibition. Further investigation into the differences between the two AN models revealed that the broadened tuning of the auditory nerve fibers produced by the Zilany & Bruce model at higher stimulus intensities coupled with the lower excitation thresholds accounted for the discrepancies. Changes in DCN model connection parameters and their effects on the resulting simulated neurons were then explored in an attempt to provide a qualitative framework for determining a set of DCN parameters capable of reproducing physiological responses using the Zilany and Bruce AN fiber model. A mix of increased W-cell inhibition to narrow the response of type-II cells and changes in AN connection parameters to increase their excitability at higher intensities were found to be required to reproduce physiological response properties. These changes in the type-II cell response would provide adequate compensation for the increased excitation and broadened responses at higher intensities observed in DCN model P-cells.
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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) | 0.001 | 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".