Control of ankle extensor muscle activity in walking cats
Bibliographic record
Abstract
Our objective was to gain insight into the relative importance of feedforward control and different proprioceptive feedback pathways to ongoing ankle extensor activity during walking in the conscious cat. We asked whether the modulation of stance phase muscle activity is due primarily to proprioceptive feedback and whether the same proprioceptive gains and feedforward commands can automatically generate the muscle activity required for changes in walking slope. To test these hypotheses, we analyzed previously collected muscle activity and mechanics data from cats with an isolated medial gastrocnemius muscle walking along a sloped pegway. Models of proprioceptor dynamics predicted afferent activity from the measured muscle mechanics. We modeled muscle activity as the weighted sum of the activity predicted from the different proprioceptive pathways and a simple model of central drive. We determined the unknown model parameters using optimization procedures that minimized the error between the predicted and measured muscle activity. We found that the modulation of muscle activity within the stance phase and across walking slopes is indeed well described by neural control that employs constant central drive and constant proprioceptive feedback gains. Furthermore, it is force feedback from Ib afferents that is primarily responsible for modulating muscle activity; group II afferent feedback makes a small contribution to tonic activity, and Ia afferent feedback makes no contribution. Force feedback combined with tonic central drive appears to provide a simple control mechanism for automatically compensating for changes in terrain without requiring different commands from the brain or even modification of central nervous system gains.
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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) | 0.000 | 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 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".