Realizing Synaptic Signal Transmission During Astrocyte-Neuron Interactions within the EMI Framework
Bibliographic record
Abstract
Abstract The tripartite synapse or “neural threesome” refers to the interplay in the synapse between neighbouring neurons, the synaptic cleft, and the surrounding glial cells. Despite extensive research, the effects of glial cells, such as astrocytes, on signal transduction between neurons are not fully understood. The Kirchhoff-Nernst- Planck (KNP) and Extracellular-Membrane-Intracellular (EMI) models constitute a promising framework for modeling these kinds of systems. However, they lack the neurotransmitter-related mechanisms that are necessary to bridge signal transduction across the synaptic cleft. Here, we propose an extension to the KNP-EMI model by a spatio-temporal diffusion-based description of the most prominent neurotransmitter, glutamate, that allows for investigation of the contribution of astrocytes to the functionality of the synapse. We validate our model by showing that the presence of an astrocyte in the domain affects the glutamate flux across the postsynaptic terminal, as observed physiologically. The proposed extension offers a sufficiently simple way of integrating synaptic glutamate dynamics into the KNP-EMI framework. It introduces the relevant interactions between electrical activity and diffusion processes at the tripartite synapse that are necessary to assess how astrocytes might contribute to the functionality of the synapse. This work has implications for future studies involving glial mechanisms and other charged species within the KNP-EMI framework.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".