Use of an Experimentally Derived Leadfield in the Peripheral Nerve Pathway Discrimination Problem
Bibliographic record
Abstract
The task of discriminating the neural pathways responsible for the activity recorded using a multi-contact nerve cuff electrode has recently been approached as an inverse problem of source localization, similar to EEG source localization. A major drawback of this method is that it requires a model of the nerve, and that the localization performance is highly dependent on the accuracy of this model. Using recordings from a 56-contact "matrix" cuff electrode placed on a rat sciatic nerve, we investigated a method that eliminates the need for a model, and uses instead an "experimental" leadfield constructed from a training set of experimental recordings. The resulting pathway-identification task is solved using an inverse problem framework. The experimental leadfield approach was able to identify the correct branch in cases in which a single fascicle was active with a success rate of 94.2%, but was not able to reliably identify combinations of fascicles. Nevertheless, the proposed methodology provides a framework for the study of multi-pathway discrimination, within which methods to improve performance can be investigated. Specifically, the influence of nerve anatomy and electrode design should be examined, and regularization approaches better suited to this novel inverse problem should be sought.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".