Modeling Electrical Activity of a Neuron: A Bond Graph Approach
Bibliographic record
Abstract
In many neurological diseases, cell bioelectrical activity is disturbed. This disorder can be caused by changes in the number of neural cell or physical, chemical and electrical properties of cell. Neural tissue modeling can be used to characterize which cell property variation leads to change in neural tissue activity and thus the disease. For a model of neural tissue, the fundamental step is to have a model of single neuron. Therefore, the purpose of this study was to model a neuron and investigate its behavior under initial excitation. Bond graph method was used to develop the neural model based on cable theory and the numerical values resulting from cellular electrophysiology experiments. Initial excitation was applied by means of step and square current functions. Eventually, action potential made along the neuron for both initial excitations was estimated. Changing in the density of ion channels which might leads to some neuropathy was considered to study its effect on action potential alterations, and the results were compared for six different densities. This evaluation revealed that the model developed in this study has the ability to distinguish between different levels of ion channel density.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".