Burst predicting neurons survive an in vitro glutamate injury model of cerebral ischemia
Bibliographic record
Abstract
Neuronal activity in vitro exhibits network bursts characterized by brief periods of increased spike rates. Recent work shows that a subpopulation of neurons reliably predicts the occurrence of network bursts. Here, we examined the role of burst predictors in cultures undergoing an in vitro model of cerebral ischemia. Dissociated primary cortical neurons were plated on multielectrode arrays and spontaneous activity was recorded at 17 days in vitro (DIV). This activity was characterized by neuronal avalanches where burst statistics followed a power law. We identified burst predictors as channels that consistently fired immediately prior to network bursts. The timing of these predictors relative to bursts followed a skewed distribution that differed sharply from a null model based on branching ratio. A portion of cultures were subjected to an excitotoxic insult (DIV 18). Propidium iodine and fluorescence imaging confirmed cell death in these cultures. While the insult did not alter the distribution of avalanches, it resulted in alterations in overall spike rates. Burst predictors, however, maintained baseline levels of activity. The resilience of burst predictors following excitotoxic insult suggests a key role of these units in maintaining network activity following injury, with implications for the selective effects of ischemia in the brain.
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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 | 0.001 |
| 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".