Localization of Active Pathways in Peripheral Nerves: A Simulation Study
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Bibliographic record
Abstract
A methodology is investigated for determining the location of active pathways in a peripheral nerve using measurements from a multicontact cuff electrode. The problem is treated as an inverse problem of source localization and solved using the sLORETA algorithm, developed for the electroencephalogram/magnetoencephalogram source localization problem. Simulated measurements are generated corresponding to action potentials traveling along either one or three pathways in a rat sciatic nerve. The performance of the proposed methodology using these measurements is evaluated in terms of localization error, missed pathways, and spurious pathways. The source localization performance when assuming an idealized nerve anatomy is compared to that when the correct anatomy is known. The effect of a spatio-temporal constraint based on the nerve anatomy and electrophysiology is also investigated. The approach in its present form was not found to be sufficiently reliable for subfascicular localization in practice, due to mean localization errors in the 140-180 mum range, high numbers of spurious pathways, and low resolution. Nonetheless, the constraints were shown to produce a marked reduction in the number of spurious pathways. Conditions under which the source localization approach may be useful for peripheral nerves are discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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