Physiology of Sleep and Wakefulness as it Relates to the Physiology of Epilepsy
Bibliographic record
Abstract
This paper reviews the present knowledge about the cellular origins of vigilance states (wakefulness and slow-wave sleep) from the perspective of their involvement in the triggering of epileptic seizures. The data stem from intracellular recordings (most of them dual impalements of pairs of neurons and glia), extracellular ionic concentrations (mainly K and Ca ) and simultaneous intracortical field potentials from the cortex of cats. These data were corroborated with recordings from naturally sleeping animals and humans. It is shown that sleep is dominated by a cortically generated slow (<1 Hz) oscillation resulting from the complex interplay within networks of neurons and glia, which are modulated by the more diffuse action of extracellular currents of ions. Wakefulness is produced through the activation of brainstem and basal forebrain structures, which disrupt sleep oscillations and elicit a global change of the extraneuronal milieu, with profound modifications of glial and cerebral blood flow parameters. Paroxysmal events arising during quiet sleep evolve within the cortex from normal slow sleep oscillations. The synchronization of large cortical and eventually subcortical territories relies on the propagation of increased currents of K through the glial syncytium, which compensate for the reduced synaptic efficacy due to the depletion of extracellular Ca.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".