Computational study of synchrony in fields and microclusters of ephaptically coupled neurons
Bibliographic record
Abstract
Neuronal hypersynchrony is implicated in epilepsy and other diseases. The low-frequency, spatially averaged electric fields from many thousands of neurons have been shown to promote synchrony. It remains unclear whether highly transient, spatially localized electric fields from single action potentials (ephaptic coupling) significantly affect spike timing of neighboring cells and in consequence, population synchrony. In this study, we simulated the extracellular potentials and the resulting coupling between neurons in the NEURON environment and generalized their connection rules to create an oscillator network model of a sheet of ephaptically coupled neurons. With the use of both models, we explained several aspects of epileptiform behavior not previously modeled by synaptically coupled networks. Importantly, reduction of neuron spacing induced synchronization via single-spike ephaptic coupling, agreeing with seizure suppression seen clinically and in vitro via extracellular volume adjustment. Further reduction of neuron spacing yielded locally synchronized clusters, providing a mechanism for recent in vitro observations of localized neuronal synchrony in the absence of synaptic and gap-junction coupling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".