Bibliographic record
Abstract
Recordings from neuronal preparations, either in vitro or in the intact brain, are characterized by fluctuations, what is commonly considered as “noise”. Due to the current recording and analysis methods, it is not feasible to separate what we term noise, from the “meaningful” neuronal activity. We propose that fluctuations serve to maintain brain activity in an optimal state for cognitive processing, not allowing it to fall into long‐term periodic behaviour. We have studied fluctuations in magnetoencephalographic (MEG) recordings from normal subjects and epileptic patients, in electroencephalographic (EEG) recordings from children with impact injury, as well as in intracerebral electrophysiological recordings in freely moving rats. Specifically, we have determined phase locking patterns between brain areas from these recordings, which display fluctuations at different scales. We submit the idea that the variability in phase synchronization affords a more complete search of all possible phase differences in a hypothetical phase‐locking state space that contributes to brain information processing. In brain pathologies, like epileptiform activity here studied, different levels of fluctuations in phase synchrony may favour the generation of stable synchronized states that characterize epileptic seizures. While the border between noise and high‐dimensional dynamics is fuzzy, the scrutiny of neuronal fluctuations at different levels will provide important insights to the unravelling of the relation between brain and behaviour.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".