Midbrain Frequency Representation following Moderately Intense Neonatal Sound Exposure in a Precocious Animal Model (<i>Chinchilla laniger</i>)
Bibliographic record
Abstract
Auditory brain areas undergo reorganization resulting from abnormal sensory input during early postnatal development. This is evident from studies at the cortical level but it remains unclear whether there is reorganization in the auditory midbrain in a species similar to the human, that is, with early hearing onset. We have explored midbrain plasticity in the chinchilla, a precocious species that matches the human in terms of hearing development. Neonatal chinchillas were chronically exposed to a 2 kHz narrowband sound at 70 dB SPL for 4 weeks. Tonotopic maps in inferior colliculus (central nucleus) were defined based on single neuron characteristic frequency. We hypothesized an overrepresentation of the 2 kHz region of the maps. However, we observed a significant decrease in the proportion of neurons dedicated to the 2 kHz octave band and also away from the exposure frequency at 8 kHz. In addition, we report a significant increase in low frequency representation (<1 kHz), again a change to tonotopic mapping distant to the 2 kHz region. Thus in a precocious species, tonotopic maps in auditory midbrain are altered following abnormal stimulation during development. However, these changes are more complex than the overrepresentation of exposure related frequency regions that are often reported.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".