Structured and unstructured reactivations during REM sleep are modulated by novel experiences
Bibliographic record
Abstract
Abstract Mammalian sleep alternates between rapid eye movement (REM) and non-REM (NREM) phases, each with distinct patterns of neural activity. The replay of waking experience during NREM sleep is a well-established substrate for memory consolidation, but whether comparable reactivation occurs during REM sleep has remained unclear, owing to the sparse firing that characterizes this state and the lack of robust methods for detecting replay within it. Here we combined calcium imaging with electrophysiology to monitor large populations of hippocampal CA1 neurons in mice and applied complementary analytical approaches to test for neural reactivation during REM sleep. We found that structured waking activity is re-expressed during REM sleep across a range of temporal organizations, from the reactivation of co-active cell assemblies to temporally ordered sequential replay. This reactivation was selectively shaped by the behavioral context of prior experience. Novel experience enhanced sequential replay and refined assembly composition, selectively reducing the participation of weakly contributing neurons; anxiogenic experience, by contrast, increased the frequency of assembly reactivation and reinforced the recruitment of strongly contributing neurons. REM sleep thus emerges as an active stage of memory processing that reorganizes recent experience according to its behavioral significance, engaging distinct operations for novel and for emotionally salient events. Significance statement Replay of waking neural activity during non-REM (NREM) sleep is strongly linked to memory consolidation, but technical and computational obstacles have left the role of REM sleep largely unresolved. Combining large-scale hippocampal recordings across wakefulness and REM sleep with complementary computational approaches, we show that REM sleep robustly reactivates activity patterns formed during awake experience. This reactivation is shaped by the behavioral context of the preceding experience, with novelty and anxiety engaging distinct forms. These findings identify REM sleep as an active brain state that engages distinct operations for novel and for emotionally salient experiences, offering new insight into how REM sleep may contribute to memory consolidation.
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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".