High-frequency signals: a comparison between the cable equation and telegrapher’s equations in nerves
Bibliographic record
Abstract
Transmission of electrical impulses along axons is commonly modelled with the cable equation, which neglects the inductive effects that have been measured in nerves. By using the telegrapher's equations, it is possible to incorporate inductive effects and compare with the non-inductive case. Although both of these approaches have been extensively studied, the question remains as to which of these provides a more accurate model of human physiology. Many of the electrical properties of nerves are frequency-dependent, a fact which is not very relevant in a low-frequency domain, but which becomes salient when higher frequencies are considered, and necessitates the exploration of the magnitude of their effects. We compare the effects of both inductance and other variable parameters across a wide frequency range using both the cable equation and the telegrapher's equations, demonstrating that it is possible for axons to transmit high-frequency signals much more effectively than might be expected, especially in the absence of an action potential. This implies that the high-frequency domain necessitates use of the more complete model.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".