Selective motor unit recruitment via intrafascicular multielectrode stimulation
Bibliographic record
Abstract
Recruitment of force via independent asynchronous firing of large numbers of motor units produces the grace and endurance of physiological motion. We have investigated the possibility of reproducing this physiological recruitment strategy by determining the selectivity of access to large numbers of independent motor units through intrafascicular multielectrode stimulation (IFMS) of the peripheral nerve. A Utah Slanted Electrode Array containing 100, 0.5-1.5 mm-long penetrating electrodes was inserted into the sciatic nerve of a cat, and forces generated by the 3 heads of triceps surea in response to electrical stimulation of the nerve were monitored via force transducers attached to their tendons. We found a mean of 17.4 +/- 4.9 (mean +/- SEM) electrodes selectively excited maximal forces in medial gastrocnemius before exciting another muscle. Among electrodes demonstrating selectivity at threshold, a mean of 7.3 +/- 2.7 electrodes were shown to recruit independent populations of motor units innervating medial gastrocnemius (overlap < 20%). Corresponding numbers of electrodes were reported for lateral gastrocnemius and soleus, as well. We used these stimulation data to emulate physiological recruitment strategies, and found that independent motor unit pool recruitment approximates physiological activation more closely than does intensity-based recruitment or frequency-based recruitment.
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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 | 0.000 | 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) | 0.001 | 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".