Slow-wave sleep alters the stability landscape of synaptic-weight space allowing life-long learning
Bibliographic record
Abstract
ABSTRACT Sleep replay - the reactivation of memory traces during slow-wave sleep - is widely held to stabilize memories and reduce interference, yet exactly how replay reorganizes synaptic-weight space to preserve existing memories while incorporating new ones remains unclear. Here, we use a biophysically realistic network model to probe the synaptic-weight dynamics underlying this process. We find that replay drives synaptic weights toward stable configurations - synaptic attractors - that jointly support both old and new memories. Hippocampus-driven interactions between sharp-wave ripples and cortical slow waves guide this reorganization, allowing recently acquired memories to be incorporated without degrading prior ones. These results reveal a mechanistic and geometric framework for memory consolidation: sleep does not passively protect memories, but actively sculpts synaptic-weight space into attractor configurations from which forgetting requires escaping a stability basin. SIGNIFICANCE STATEMENT Storing, processing, and retrieving information underpins intelligent behavior. Sleep extracts invariant features from prior experience, promoting the emergence of explicit knowledge and insight. Yet despite abundant empirical findings, our understanding of how sleep reshapes memory representations at the level of synaptic organization remains limited. Here we present a novel framework that describes how memories are encoded in synaptic-weight space and how sleep dynamics reorganize synaptic landscape. These results advance our understanding of how the brain solves core problems of lifelong learning.
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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) | 0.002 | 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".