Singing modulates parvalbumin interneurons throughout songbird forebrain vocal control circuitry
Bibliographic record
Abstract
Across species, the performance of vocal signals can be modulated by the social environment. Zebra finches, for example, adjust their song performance when singing to females ('female-directed' or FD song) compared to when singing in isolation ('undirected' or UD song). These changes are salient, as females prefer the FD song over the UD song. Despite the importance of these performance changes, the neural mechanisms underlying this social modulation remain poorly understood. Previous work in finches has established that expression of the immediate early gene EGR1 is increased during singing and modulated by social context within the vocal control circuitry. Here, we examined whether particular neural subpopulations within those vocal control regions exhibit similar modulations of EGR1 expression. We compared EGR1 expression in neurons expressing parvalbumin (PV), a calcium buffer that modulates network plasticity and homeostasis, among males that performed FD song, males that produced UD song, or males that did not sing. We found that, overall, singing but not social context significantly affected EGR1 expression in PV neurons throughout the vocal control nuclei. We observed differences in EGR1 expression between two classes of PV interneurons in the basal ganglia nucleus Area X. Additionally, we found that singing altered the amount of PV expression in neurons in HVC and Area X and that distinct PV interneuron types in Area X exhibited different patterns of modulation by singing. These data indicate that throughout the vocal control circuitry the singing-related regulation of EGR1 expression in PV neurons may be less influenced by social context than in other neuron types and raise the possibility of cell-type specific differences in plasticity and calcium buffering.
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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".