Computer simulations of high-pass filtering in zebrafish larval muscle fibres
Bibliographic record
Abstract
Larval somatic muscle of the zebrafish, Danio rerio, like that of some other organisms, responds to a sustained depolarization with one, and only one, action potential. Here, we report computer simulations, using the NEURON simulation programme, of sodium and potassium currents of somatic muscle of larval Danio rerio to investigate their possible contribution to once-only firing. Our computer model incorporated simulated sodium and potassium ion channels based on steady-state and kinetic parameters derived from a recent electrophysiological study. The model responded to sustained depolarizations with a single action potential at all levels of depolarization above threshold. By varying several parameters of the sodium and potassium currents systematically, the minimum changes necessary to produce repetitive firing were found to be a positive shift in the half-inactivation and a negative shift in the half-activation potentials for the sodium current, accompanied by a slowing of the rate of inactivation to half of the experimentally observed values. This suggests that once-only spiking can be attributed to the steady-state values of activation and inactivation of the sodium current, along with a slower rate of inactivation. Mapping of the resultant firing properties against steady-state and kinetic ion channel parameters revealed a high safety factor for once-only firing and showed that the time constant of inactivation of the sodium current was the key determinant of once-only or repetitive firing. The rapidly inactivating potassium current does not influence once-only firing or the maximum rate of firing in response to periodic excitation in these simulations. Although a contribution of other currents to produce once-only firing has not been excluded, this model suggests that the properties of the sodium current are sufficient to account for once-only firing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".