Evaluating a possible role for persistent inward currents in firing rate saturation
Bibliographic record
Abstract
During increasing isometric force contractions, motor unit firing rates (FRs) tend to increase rapidly and then saturate (that is, reach a plateau), despite continued augmentation in force and, presumably, synaptic drive. The mechanism underlying this behavior is presently unknown, although persistent inward current (PIC) activation may provide one possible explanation. PICs contribute a non‐synaptic excitation source that activates around recruitment threshold and boosts synaptic input. Furthermore, PICs appear to be inactivated by inhibitory input. Therefore, we reasoned that if we artificially delivered synaptic inhibition to motor neurons before a ramp contraction, then this might prevent full expression of PICs and prevent the initial rapid increase in FR. To test this hypothesis, human subjects performed isometric triangular ramp contractions using tibialis anterior in the absence and presence of additional inhibition provided by surface sural nerve stimulation. We compared single motor unit FR profiles, expressed as a function of force, by fitting the data with rising exponential and linear functions. During control trials, ascending FR profiles were best fit by a rising exponential function but became more linearly related to force with sural nerve stimulation. Overall, our results are consistent with the hypothesis that PIC activation contributes to FR saturation. Funding from NIH NS070897
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".