A novel coding mechanism for social vocalizations in the lateral amygdala
Bibliographic record
Abstract
The amygdala plays a central role in evaluating the significance of acoustic signals and coordinating the appropriate behavioral responses. To understand how amygdalar responses modulate auditory processing and drive emotional expression, we assessed how neurons respond to and encode information that is carried within complex acoustic stimuli. We characterized responses of single neurons in the lateral nucleus of the amygdala to social vocalizations and synthetic acoustic stimuli in awake big brown bats. Neurons typically responded to most of the social vocalizations presented (mean = nine of 11 vocalizations) but differentially modulated both firing rate and response duration. Surprisingly, response duration provided substantially more information about vocalizations than did spike rate. In most neurons, variation in response duration depended, in part, on persistent excitatory discharge that extended beyond stimulus duration. Information in persistent firing duration was significantly greater than in spike rate, and the majority of neurons displayed more information in persistent firing, which was more likely to be observed in response to aggressive vocalizations (64%) than appeasement vocalizations (25%), suggesting that persistent firing may relate to the behavioral context of vocalizations. These findings suggest that the amygdala uses a novel coding strategy for discriminating among vocalizations and underscore the importance of persistent firing in the general functioning of the amygdala.
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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 | 0.001 |
| 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".