Noise-like fluctuations drive shifts in 1/ f α power-law dynamics associated with changes in brain states
Bibliographic record
Abstract
The aperiodic, broadband components of many electrophysiological recordings display a characteristic 1/ f α powerlaw scaling. This highly debated feature of the power spectral density (PSD), observed in many neural systems, exhibits substantial variability across behavioral states, levels of arousal, and pharmacological interventions. Fluctuations in the critical exponent α have been linked to transitions between distinct brain states, ranging from synchronized, slow-wave dynamics during sleep to desynchronized activity during wakefulness, for instance. We investigated the origins and variability of this 1/ f α power-law scaling using large-scale, recurrent neural networks model with sparse, balanced and random connectivity, well suited to capture the diversity in dynamics, structure and scale characteristic of neural systems exhibiting such critical-like behavior. By integrating recent advances in random matrix theory, we develop an approach to determine critical features of the PSD by accounting for both nonlinear and stochastic contributions. These proved to be essential in demonstrating that variability in power-law scaling is a generic feature of random, nonlinear networks exposed to changes in additive noise. These results provide insight as to the mechanisms shaping macroscopic spectral signatures of varying brain states.
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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 | 0.002 |
| 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 | 0.001 |
| 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".