A framework for the discrimination of neural pathways using multi-contact nerve cuff electrodes
Bibliographic record
Abstract
Monitoring the activity of specific neural pathways in a peripheral nerve is a task with numerous applications in implanted neuroprosthetic systems. Achieving selective recording using multi-contact nerve cuff electrodes is appealing because these devices are well suited for chronic use, but no viable general solution to the task of discriminating combinations of active pathways from extra-neural recordings has yet been proposed. Bioelectric source localization approaches have been suggested, but their effectiveness is limited by the accuracy of the nerve model used to solve the forward problem. We propose a model-free alternative to the pathway discrimination task, in which experimental data is used to estimate a solution to the forward problem. The method was evaluated using a 56-channel cuff placed on the rat sciatic nerve. 3 pathways were discriminated with a 94.2% success rate when individually active, whereas further improvements are needed in order to recover combinations of simultaneously active pathways.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".