Seizure-like events in rodent and computer models: A ring device perspective
Bibliographic record
Abstract
Epileptiform activity involves abrupt changes in dynamic behaviour of neuronal ensembles, which alternates between higher complexity `interictal' mode and lower-complexity `ictal' mode characterized by dense, rhythmic firing of the seizing network. Three mechanisms for generating seizurelike events (SLEs) in populations of coupled oscillators will be highlighted as state transitions from higher to lower complexity modes. (i) System parameter changes can cause transitions by way of bifurcations. (ii) Noise fluctuations cause state transitions in bistable systems. This is how paroxysmal transitions are explained in a bistable model of absence epilepsy. (iii) The cognitive rhythm generator network model exhibits intermittency in the absence of either system parameter changes or noise fluctuations. Under simulated epileptogenic conditions, transitions occur unprovoked between the interictal and ictal modes of a chaotic attractor with the trajectory visiting the neighborhood of each mode intermittently. Network "excitability" effects both local and global bifurcations in the dynamics, and under hyperexcitable conditions a bimodal epileptiform attractor is exhibited. This study describes a unified approach to the three mechanisms via a ring device perspective.
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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 | 0.001 |
| 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".