Pairing Cholinergic Enhancement with Perceptual Training Promotes Recovery of Age-Related Changes in Rat Primary Auditory Cortex
Bibliographic record
Abstract
We used the rat primary auditory cortex (A1) as a model to probe the effects of cholinergic enhancement on perceptual learning and auditory processing mechanisms in both young and old animals. Rats learned to perform a two-tone frequency discrimination task over the course of two weeks, combined with either the administration of a cholinesterase inhibitor or saline. We found that while both age groups learned the task more quickly through cholinergic enhancement, the young did so by improving target detection, whereas the old did so by inhibiting erroneous responses to nontarget stimuli. We also found that cholinergic enhancement led to marked functional and structural changes within A1 in both young and old rats. Importantly, we found that several functional changes observed in the old rats, particularly those relating to the processing and inhibition of nontargets, produced cortical processing features that resembled those of young untrained rats more so than those of older adult rats. Overall, these findings demonstrate that combining auditory training with neuromodulation of the cholinergic system can restore many of the auditory cortical functional deficits observed as a result of normal aging and add to the growing body of evidence demonstrating that many age-related perceptual and neuroplastic changes are reversible.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".