Dynamical modulation of theta-gamma coupling during REM sleep
Bibliographic record
Abstract
Theta phase modulates gamma amplitude during spatial navigation and rapid eye movement sleep (REMs). Although the REMs theta rhythm has been linked to spatial memory consolidation, the underlying mechanism remains unclear. We investigate dynamics of theta-gamma interactions across multiple frequency and temporal scales in simultaneous recordings from hippocampal CA3, CA1, subiculum, and parietal cortex. We show that theta phase significantly modulates three distinct gamma bands during REMs, dynamically. Interestingly, we further show that theta-gamma coupling swings between different hippocampal and cortical structures during REMs and tends to increase over a single REMs episode. Comparing to active wake, theta-gamma coupling during REMs is significantly increased for subicular and cortical, but not for CA3 and CA1, recordings. Finally, optogenetic silencing of septohippocampal GABAergic projections significantly impedes both theta-gamma coupling and theta phase coherence, two neural mechanisms of working and long-term memory, respectively. Thus, we show that theta-gamma coupling and theta phase coherence are highly modulated during single REMs episode and propose that theta-gamma coupling provides a predominant mechanism for information processing within each brain region, while the orchestrated nature of coupling activity establishes a specific phase-space coding of information during sleep.
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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) | 0.000 | 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".