Use of an Experimentally Derived Leadfield in the Peripheral Nerve Pathway Discrimination Problem
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it