The effect of input noises on the activity of auditory neurons using GLM-based metrics <sup>*</sup>
Bibliographic record
Abstract
Abstract Objective. The auditory system is extremely efficient in extracting auditory information in the presence of background noise. However, people with auditory implants have a hard time understanding speech in noisy conditions. The neural mechanisms related to the processing of background noise, especially in the inferior colliculus (IC) where the auditory midbrain implant is located, are still not well understood. Understanding the mechanisms of perception in noise could lead to better stimulation or preprocessing strategies for such implants. We thus wish to investigate if there is a difference in the activity of neurons in the IC when presenting noisy vocalizations with different types of noise (stationary vs. non-stationary), input signal-to-noise ratios (SNR) and signal levels. Approach. We developed novel metrics based on a generalized linear model (GLM) to investigate the effect of a given input noise on neural activity. We used these metrics to analyze neural data recorded from the IC in ketamine-anesthetized female Hartley guinea pigs while presenting noisy vocalizations. Main results. We found that non-stationary noise clearly contributes to the multi-unit neural activity in the IC by causing excitation, regardless of the SNR, input level or vocalization type. However, when presenting white or natural stationary noises, a great diversity of responses was observed for the different conditions, where the multi-unit activity of some sites was affected by the presence of noise and the activity of others was not. Significance. The GLM-based metrics allowed the identification of a clear distinction between the effect of white or natural stationary noises and that of non-stationary noise on the multi-unit activity in the IC. This had not been observed before and indicates that the so-called noise invariance in the IC is dependent on the input noisy conditions. This could suggest different preprocessing or stimulation approaches for auditory midbrain implants depending on the noisy conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".