The Temporal-Spatial Effects of Sounds in the Mammalian Auditory Midbrain
Bibliographic record
Abstract
Due to the excessive use of portable audio devices and the increased exposures to industrial noises, there is an increasing likelihood of Canadians being afflicted with hearing problems. In Canada, hearing disorders remain as undertreated health concerns. To progress hearing healthcare, we must understand the changes caused by hearing damages on the auditory neural pathways. We will first investigate the neural mechanisms involved in hearing. Previous studies have suggested that the perception of one sound can be changed by another sound, regardless of the locations or timings of the sounds. We are currently studying an important phenomenon. Our topic explores the neural responses involved in how a sound can be affected by a preceding sound and how this effect is dependent on the temporal-spatial relationships between the two sounds. Neurophysiological responses are recorded from both individual and populations of neurons in the rat’s auditory midbrain. The preliminary results have shown that a preceding sound could reduce the responses to a trailing sound as the time gaps between the two sounds decreases. As the number of presentations of the preceding sound increases, a longer time gap is needed for the trailing sound to be unaltered. Furthermore, the effects of the preceding sound on the neural responses to the trailing sound were reduced as the spatial separation increases between the two sounds. Our research into the neural mechanisms of hearing will help us understand how the brain perceives sounds in a natural environment. Using these results, clinical researchers can enhance the effects of cochlear implants and develop advanced hearing-enhancing devices, which will aid in improving the quality of life for our community.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".