A Mathematical Framework for Integrating Soliton Sensitivity into the Hodgkin-Huxley Model
Bibliographic record
Abstract
Although the Hodgkin-Huxley model is ubiquitously used for predicting axonal signal transmission in the central nervous system, it has a number of notable shortcomings, such as its inability to predict the effect of certain anesthetics and an inability to completely describe saltatory conduction. Competing models have thus emerged including the soliton, action wave, and softmaterial waveguide models, all of which describe information transmission as propagating physical distortions in the neuronal membrane rather than a traveling action potential; these mechanical models have their own shortcomings, being unable to predict the effects of ion channel blockers. Herein, we propose the Soliton-Sensitive Hodgkin Huxley framework as a unified electricalmechanical construct that incorporates the action wave model's thermodynamic description of membrane displacement into the mathematical formalism of Hodgkin-Huxley. Using this framework, hybrid models can be constructed that retain the simplicity of the Hodgkin-Huxley model, while augmenting its robustness such that it can reproduce experimental observations in systems wherein the physical structure of the neuronal membrane is altered. This notion of incorporating mechanical information as input to generate electrical signal output (action potentials) represents a promising avenue for the development of a new generation of simple hybrid models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".