Perineuronal nets in motor circuitry regulate the performance of learned vocalizations in songbirds
Bibliographic record
Abstract
The accurate and reliable performance of learned vocalizations (e.g., speech and birdsong) modulates the efficacy of communication in humans and songbirds. Consequently, it is critical to understand the factors that regulate the performance of learned vocalizations. Across taxa, neural circuits underlying motor learning and control are replete with perineuronal nets (PNNs), and we analyzed how PNNs in vocal motor circuitry regulate the performance of learned song in zebra finches. We report that developmental increases in PNN expression in vocal circuitry are associated with developmental increases in song stereotypy. We also document that enzymatically degrading PNNs in the motor nucleus HVC acutely altered song structure (changes in syllable sequencing and production). Collectively, our data reveal a causal contribution of PNNs to the performance of learned behaviors and, given the parallels in the regulation of birdsong and speech, suggest that PNNs in motor circuitry could modulate speech performance. Manipulating perineuronal nets (PNNs) in the motor nucleus HVC of zebra finches alters the structure of vocalizations acquired during development, highlighting a causal contribution of PNNs in motor circuitry to the performance of learned behaviors.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".