Sleep and wakefulness in rats: probabilities of state transitions and state-to-state serial correlations in relation to circadian and ultradian phases
Bibliographic record
Abstract
Abstract Sleep and wakefulness were recorded from undisturbed male rats (n = 10) for 7 days in a 12:12 h light-dark cycle. Data were partitioned by circadian and ultradian phase before quantification of within-state transition probabilities and between-state serial correlations. Within each joint circadian-ultradian phase, short-term statistical patterns were identified, with quantitative variation between phases. Statistical “signatures” of sleep-wake pattern include positive serial correlations between durations of contiguous states, and between state bouts and inter-bout intervals. There was no correlation between wake bout duration and subsequent sleep episode duration, contradicting an oft-cited assumption of the sleep homeostasis hypothesis. The data confirm a positive correlation between the duration of rapid eye movement (REM) sleep and duration of the subsequent inter-REM interval, which is traditionally interpreted as evidence for a short-term REM homeostat. The present study concludes that the REM homeostat hypothesis is underdetermined. It is suggested that this serial correlation and other sleep-wake patterns can be explained by an alternative non-homeostatic mechanism; transition probability prevailing at the end of a state episode is carried over to the beginning of the next state (“inter-state transition momentum”). The plausibility of this hypothesis was supported by preliminary simulations using a 2nd-order 3-state semi-Markov model. Highlights Wake-related transition probabilities underly circadian and ultradian rhythms. Statistical signatures of short-term patterns of sleep-wake state were identified in all circadian and ultradian phases. Transition probabilities varied with time in state. State transition probabilities were influenced by duration and identity of prior state. Sleep homeostasis hypotheses were not supported. A hypothesis of state-spanning transition momentum is proposed.
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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".