Mechanisms and functional implications of motoneuron adaptations to increased physical activity
Bibliographic record
Abstract
Motoneurons demonstrate adaptations in their physiological properties to alterations in chronic activity levels. The most consistent change that appears to result from endurance-type exercise training is the reduced excitatory current required to initiate and maintain rhythmic firing. While the precise mechanisms through which these neurons adapt to activity are currently unknown, evidence exists that adaptation may involve alterations in the expression of genes that code for membrane receptors, which can influence the responses of neurons to transmitters during activation. The influence of these adaptations may also extend to the resting condition, where ambient levels of neuroactive substances may influence ion conductances at rest, and thus result in the activation or inhibition of specific ion conductances that underlie the measurements of increased excitability that have been reported for motoneurons in the anesthetised state. We have applied motoneuron excitability and muscle unit contractile changes with endurance training to a mathematical computerized model of motor unit recruitment (Heckman and Binder 1991; J. Neurophysiol. 65(4):952-967). The results from the modelling exercise demonstrate increased task efficiency at relative levels of effort during a submaximal contraction. The physiological impact that nerve and muscle adaptations have on the neuromuscular system during standardized tasks seem to fit with reported changes in motor unit behaviour in trained human subjects.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".