Critical period regulation across multiple timescales
Bibliographic record
Abstract
Brain plasticity is dynamically regulated across the life span, peaking during windows of early life. Typically assessed in the physiological range of milliseconds (real time), these trajectories are also influenced on the longer timescales of developmental time (nurture) and evolutionary time (nature), which shape neural architectures that support plasticity. Properly sequenced critical periods of circuit refinement build up complex cognitive functions, such as language, from more primary modalities. Here, we consider recent progress in the biological basis of critical periods as a unifying rubric for understanding plasticity across multiple timescales. Notably, the maturation of parvalbumin-positive (PV) inhibitory neurons is pivotal. These fast-spiking cells generate gamma oscillations associated with critical period plasticity, are sensitive to circadian gene manipulation, emerge at different rates across brain regions, acquire perineuronal nets with age, and may be influenced by epigenetic factors over generations. These features provide further novel insight into the impact of early adversity and neurodevelopmental risk factors for mental disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".