Quantitative input-output relationships between human soleus muscle spindle afferents and motoneurons
Bibliographic record
Abstract
A method is described that, for the first time, allows instantaneous estimation of the Ia fiber input to human soleus motoneurons following electrical stimulation of the tibial nerve. The basis of the method is to determine the thresholds of the most and least excitable 1a fibers to electrical stimulation, and to treat the intervening thresholds as having a normal distribution about the mean; the validity of this approach is discussed. It was found that, for the same Ia fiber input, the percentage of soleus motoneurons contributing to the H (Hoffmann)-reflex differed considerably among subjects; when the results were pooled, however, there was an approximately linear relationship between Ia input and motoneuron output. Weak extension of the great toe diminished the soleus motoneuron reflex discharge in all but 2 of 16 subjects; the results for weak ankle plantarflexion were less consistent, but overall, there was a reduction in soleus motoneuron output also. The methodology should provide new insights into disorders of movement and tone, especially as it permits estimates of motoneuron depolarization to be made. NEW & NOTEWORTHY Assuming a normal distribution of Ia fiber thresholds to electrical stimulation and using the H-reflex, we determined for the first time an Ia input-α-motoneuron output relationship for the human soleus muscle. The relationship varies greatly among subjects but, overall, is approximately linear. Minimal contraction of a toe muscle alters the relationship dramatically, probably due to presynaptic inhibition of Ia fibers. Drawing on the literature, we can calculate changes in α-motoneuron membrane potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".