Cell-type specific burst firing interacts with theta and beta activity in prefrontal cortex during attention states
Bibliographic record
Abstract
Abstract Population-level theta and beta band activity in anterior cingulate and prefrontal cortex (ACC/PFC) are prominent signatures of endogenously controlled, adaptive behaviors. But how these rhythmic activities are linked to cell-type specific activity has remained unclear. Here, we suggest such a cell-to-systems level linkage. We found that the rate of burst spiking events is enhanced particularly during attention states and that attention-specific burst spikes have a unique temporal relationship to local theta and beta band population level activities. For the 5-10Hz theta frequency range, bursts coincided with transient increases of local theta power relative to non-bursts, particularly for bursts of putative interneurons. For the 16-30Hz beta frequency, bursts of putative interneurons phase synchronized stronger than nonbursts, and were associated with larger beta power modulation. In contrast, burst of putative pyramidal cells were overall similarly beta-synchronized than nonbursts, but were linked with stronger beta power only when they occurred early in the beta cycle. These findings suggests that in the ACC/PFC during attention states, mechanisms underlying burst firing are intimately linked to narrow band population level activities, providing a cell-type specific window into the emergence, resetting, or termination of oscillatory activities.
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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".