Neocortical Very Fast Oscillations (Ripples, 80–200 Hz) During Seizures: Intracellular Correlates
Bibliographic record
Abstract
Multi-site field potential and intracellular recordings from various neocortical areas were used to study very fast oscillations or ripples (80-200 Hz) during electrographic seizures in cats under ketamine-xylazine anesthesia. The animals displayed spontaneously occurring and electrically induced seizures comprising spike-wave complexes (2-3 Hz) and fast runs (10-20 Hz). Neocortical ripples had much higher amplitudes during seizures than during the slow oscillation preceding the onset of seizures. A series of experimental data from the present study supports the hypothesis that ripples are implicated in seizure initiation. Ripples were particularly strong at the onset of seizures and halothane, which antagonizes the occurrence of ripples, also blocked seizures. The firing of electrophysiologically defined cellular types was phase-locked with ripples in simultaneously recorded field potentials. This indicates that ripples during paroxysmal events are associated with a coordination of firing in a majority of neocortical neurons. This was confirmed with dual intracellular recordings. Based on the amplitude that neocortical ripples reach during paroxysmal events, we propose a mechanism by which neocortical ripples during normal network activity could actively participate in the initiation of seizures on reaching a certain threshold amplitude. This mechanism involves a vicious feedback loop in which very fast oscillations in field potentials are a reflection of synchronous action potentials, and in turn these oscillations help generate and synchronize action potentials in adjacent neurons through electrical interactions.
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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".