Contribution of sensory feedback to ongoing ankle extensor activity during the stance phase of walking
Bibliographic record
Abstract
Numerous investigations over the past 15 years have demonstrated that sensory feedback plays a critical role in establishing the timing and magnitude of muscle activity during walking. Here we review recent studies reporting that sensory feedback makes a substantial contribution to the activation of extensor motoneurons during the stance phase. Quantitative analysis of the effects of loading and unloading ankle extensor muscles during walking on a horizontal surface has shown that sensory feedback can increase the activity of ankle extensor muscles by up to 60%. There is currently some uncertainty about which sensory receptors are responsible for this enhancement of extensor activity, but likely candidates are the secondary spindle endings in the ankle extensors of humans and the Golgi tendon organs in the ankle extensors of humans and cats. Two important issues arise from the finding that sensory feedback from the leg regulates the magnitude of extensor activity. The first is the extent to which differences in the magnitude of activity in extensor muscles during different locomotor tasks can be directly attributed to changes in the magnitude of sensory signals, and the second is whether the enhancement of extensor activity is determined primarily by feedback from a specific group of receptors or from numerous groups of receptors distributed throughout the leg. Limitations of current experimental strategies prevent a straightforward empirical resolution of these issues. A potentially fruitful approach in the immediate future is to develop models of the known and hypothesized neuronal networks controlling motoneuronal activity, and use these simulations to control forward dynamic models of the musculo-skeletal system. These simulations would help understand how sensory signals are modified with a change in locomotor task and, in conjunction with physiological experiments, establish the extent to which these modifications can account for changes in the magnitude of motoneuronal activity.
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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".