Bibliographic record
Abstract
This book is essentially devoted to analyses of neuronal substrates underlying sleep with low-frequency oscillations, so-called sleep with synchronized electroencephalogram (EEG) or resting sleep, and a series of seizures that preferentially develop during this state of sleep. The reader will find data on intrinsic neuronal properties and network operations in corticothalamic, hippocampal-entorhinal and neuromodulatory systems that control forebrain normal and paroxysmal activities. This brief introductory chapter is not intended to discuss in detail the history of ideas on two bedfellows, sleep and epilepsy, but only to resurrect some of the most important figures and concepts that are directly related to what is discussed at length, essentially at the neuronal level, in the following chapters. Brain, neurons and sleep across centuries Different states of vigilance, such as waking and sleep states with or without dreams, have been recognized in ancient cultures. The deafferentation theory of falling asleep dates back to Lucretius, in the first century b.c., was revitalized in the early 19th century by Macnish and Purkinje, and was finally developed during the past century by three major figures of sleep research: Bremer, Moruzzi and Kleitman. As discussed in Chapter 3, the concept of passive or active sleep is a false dilemma as both brain deafferentation from external signals and actively sleep-inducing (humoral and neuronal) factors may lead to sleep, since the presumed actively hypnogenic neurons exert their inhibitory effects on activating neurons in the brainstem and posterior hypothalamus, thus disconnecting the forebrain, as postulated in the passive theory.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".