The role of neuromuscular properties in determining the end-point of a movement
Bibliographic record
Abstract
How does the activation of several muscles combine to produce reliable multijoint movements? To study this question, we stimulated up to six sites in muscles, nerves, and the spinal cord. Flexion and extension of the hip, knee, and ankle were elicited in anesthetized and decerebrate cats. The movements occurred largely in the sagittal plane against a constant spring load and covered most of the passive range of motion of the cat's limb. The movements of the end-point (foot) were compared with predictions based on vectorial summation of end-point movements elicited by stimulating single electrodes. The lengths of the movements produced by stimulating more than one site exceeded what was expected from linear summation for small movements (<3 cm) and showed a less than linear summation for large movements (>11 cm). The data were compared with muscle and limb models. Since the deviations from linearity were predictable as a function of distance, adjustments might easily be learned by trial and error. The summation was less complete for spinal stimulation, compared to nerve and muscle stimulation, so spinal circuits do not appear to compensate for the nonlinearities. Movements were elicited from positions of the limb not only in a neutral position, but also in front and behind the neutral position. A degree of convergence was seen, even with stimulation of some individual muscles, but the convergence increased as more muscles were stimulated and more joints were actively involved. This suggests that convergence to an equilibrium-point arises at least partly from muscle properties. In conclusion, there are deviations from linear vectorial summation, and these deviations increase when more muscles are stimulated. The convergence to an equilibrium-point may simplify the computations needed to produce movements involving many muscles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".