Precise Long-Range Synchronization of Activity and Silence in Neocortical Neurons during Slow-Wave Sleep
Bibliographic record
Abstract
Slow-wave sleep is characterized by alternating periods of activity and silence in corticothalamic networks. Both activity and silence are stable network states, but the mechanisms of their alternation remain unknown. We show, using simultaneous multisite intracellular recordings in cats, that slow rhythm involves all neocortical neurons and that both activity and silence started almost synchronously in cells located up to 12 mm apart. Activity appeared predominantly at the area 5/7 border and spread in both anterior and posterior directions. The activity started earlier in fast-spiking cells and intrinsically bursting cells than in regular-spiking neurons. These results provide direct evidence for two mechanisms of active state generation: spread of activity from a local focus and synchronization of weaker activity, originating at multiple locations. Surprisingly, onsets of silent states were synchronized even more precisely than the onsets of activity, showing no latency bias for location or cell type. This most intriguing finding exposes a major gap in understanding the nature of state alternation. We suggest that it is the synchronous termination of activity and occurrence of silent states of the neuronal network that makes the EEG picture during slow-wave sleep so characteristic. Synchronous onset of silence in distant neurons cannot rely exclusively on properties of individual cells and synapses, such as adaptation of neuronal firing or synaptic depression; instead, it implies the existence of a network mechanism. Revealing this yet unknown large-scale mechanism, which switches network activity to silence, will aid our understanding of the origin of brain rhythms in normal function and pathology.
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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) | 0.000 | 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".