Balance of synaptic and electrotonic connections controls the excitability of networks in biophysical model of epilepsy
Bibliographic record
Abstract
Recent studies have implicated astrocytes in multiple active roles in neuronal networks. In particular they have been shown to be able to moderate and alter neural firing patterns both in normal and epileptic conditions. In addition, it has been proposed that one of the roles of gap junctions between astrocytes, as well as neurons is in increasing synchronization of neuronal firing and potential epileptogenic effect. In this study we build upon a model of a network that incorporates both pyramidal cells and interneurons as well as astrocytes with potassium clearance mechanisms and basic calcium dynamics. We include electrotonic connections between cells to be able to separate the effects of synaptic connections and gap junctions on neuronal hyperexcitability. Preliminary findings of this model show that under normal conditions, when gap junctions are blocked the network exists in an interictal-like state. When the system is put in a zero calcium environment (i.e. synaptic connections are disabled), the network enters spontaneous rhythmic bursting with very regular spiking. This suggests that electrotonic connections play a crucial role in the epileptogenesis within the neuronal network.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".