Detection of Active and Silent States in Neocortical Neurons from the Field Potential Signal during Slow-Wave Sleep
Bibliographic record
Abstract
Oscillations of the local field potentials (LFPs) or electroencephalogram (EEG) at frequencies below 1 Hz are a hallmark of the slow-wave sleep. However, the timing of the underlying cellular events, which is an alternation of active and silent states of thalamocortical network, can be assessed only approximately from the phase of slow waves. Is it possible to detect, using the LFP or EEG, the timing of each episode of cellular activity or silence? With simultaneous recordings of the LFP and intracellular activity of 2-3 neocortical cells, we show that high-gamma-range (20-100 Hz) components in the LFP have significantly higher power when cortical cells are in active states as compared with silent-state periods. Exploiting this difference we have developed a new method, which uses the LFP signal to detect episodes of activity and silence of neocortical neurons. The method allows robust, reliable, and precise detection of timing of each episode of activity and silence of the neocortical network. It works with both surface and depth EEG, and its performance is affected little by the EEG prefiltering during recording. These results open new perspectives for studying differential operation of neural networks during periods of activity and silence, which rapidly alternate on the subsecond scale.
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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 | 0.000 | 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".