Noise Invariance in Inferior Colliculus Neurons is Dependant on the Input Noisy Conditions
Bibliographic record
Abstract
Abstract The auditory system is extremely efficient to extract audio information in the presence of background noise. However, the neural mechanisms related to this efficiency is still greatly misunderstood, especially in the inferior colliculus (IC). In fact, while noise processing under different conditions has been investigated at the auditory cortex level, studies in the IC have been much limited. One interesting observation has been that there seems to be some degree of noise invariance in the IC in the presence of white noise. We wish to broaden this knowledge by investigating if there is a difference in the activity of neurons in the IC, when presenting noisy vocalisations with different types of noises, input signal-to-noise ratios (SNR) and signal levels. We do so using a generalized linear model (GLM), which gives us the ability to study the neural activity under these different conditions at a per neuron level. We found that non-stationary noise is the only noise type that clearly contributes to the neural activity in the IC, regardless of the SNR, input level or vocalisation type. However, when presenting white or natural stationary noises, a great diversity of responses was observed for the different conditions, where the activity of some neurons was affected by the presence of noise and the activity of others was not. Therefore, there seems to be some level of background noise invariance as early as the IC level, as reported before, however, this invariance seems to be highly dependent on the noisy conditions. New & Noteworthy The neural mechanisms of auditory perception in the presence of background noise are still not well understood, especially in the IC. We studied neural activity in the IC when presenting noisy vocalisations using different background noise types, SNRs and input sound levels. We observed that only the non-stationary noise type clearly contributes to the neural activity in the IC. The noise invariance previously observed in the IC thus seems dependent on the noisy conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".