pH during non-synaptic epileptiform activity—computational simulations
Bibliographic record
Abstract
The excitability of neuronal networks is strongly modulated by changes in pH. The origin of these changes, however, is still under debate. The high complexity of neural systems justifies the use of computational simulation to investigate mechanisms that are possibly involved. Simulated neuronal activity includes non-synaptic epileptiform events (NEA) induced in hippocampal slices perfused with high-K(+) and zero-Ca(2+), therefore in the absence of the synaptic circuitry. A network of functional units composes the NEA model. Each functional unit represents one interface of neuronal/extracellular space/glial segments. Each interface contains transmembrane ionic transports, such as ionic channels, cotransporters, exchangers and pumps. Neuronal interconnections are mediated by gap-junctions, electric field effects and extracellular ionic fluctuations modulated by extracellular electrodiffusion. Mechanisms investigated are those that change intracellular and extracellular ionic concentrations and are able to affect [H(+)]. Our simulations suggest that the intense fluctuations in intra and extracellular concentrations of Na(+), K(+) and Cl(-) that accompany NEA are able to affect the combined action of the Na(+)/H(+) exchanger (NHE), [HCO(-)(3)]/Cl(-) exchanger (HCE), H(+) pump and the catalytic activity of intra and extracellular carbonic anhydrase. Cellular volume changes and extracellular electrodiffusion are responsible for modulating pH.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".