Perineuronal nets in motor circuitry regulate the performance of learned vocalizations in songbirds
Bibliographic record
Abstract
ABSTRACT The accurate and reliable production of learned behaviors can be important for survival and reproduction; for example, the performance of learned vocalizations (e.g., speech and birdsong) modulates the efficacy of social communication in humans and songbirds. Consequently, it is critical to understand the factors that regulate the performance of learned behaviors. Across various taxa, neural circuits that regulate motor learning are replete with perineuronal nets (PNNs), extracellular matrices that surround neurons and shape neural dynamics and plasticity. Perineuronal nets in circuits for sensory and cognitive processes have been found to affect sensory processing and behavioral plasticity. However, the function of PNNs in motor circuits remains largely unknown. Here, we analyzed the causal contribution of PNNs in motor circuitry to the performance of learned vocalizations in songbirds. Songbirds like the zebra finch are powerful models for this endeavor because the performance of their learned songs is regulated by activity within discrete and specialized circuits (i.e., song system) that are dense with PNNs. We first report that developmental increases in the density and intensity of PNNs throughout the song system [including in the motor nucleus HVC (acronym used as proper name)] are associated with developmental increases in song performance. We next discovered that enzymatically degrading PNNs in HVC acutely affected song performance. In particular, PNN degradation caused song structure to deviate from pre-surgery song due to changes in syllable sequencing and production. Collectively, our data provide compelling evidence for a causal contribution of PNNs to the performance of learned behaviors. SIGNIFICANCE STATEMENT Motor circuits are replete with perineuronal nets (PNNs) but little is known about their contribution to motor performance. Here, we analyzed how PNNs within vocal motor circuits modulate the ability of songbirds to consistently produce their learned songs. We report that developmental increases in PNN expression in vocal circuitry were associated with developmental increases in the ability to consistently perform their learned song. Moreover, degrading PNNs in the vocal motor nucleus HVC reduced the ability of adult birds to accurately produce their learned song. Our findings indicate a causal contribution of PNNs in motor circuitry to the performance of learned behaviors and, because PNNs are expressed in brain areas regulating speech, suggest that PNNs could modulate speech production in humans.
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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 | 0.000 | 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 | 0.000 |
| 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".