Rate-coding of spinal motoneurons with high-frequency magnetic stimulation of human motor cortex
Bibliographic record
Abstract
Rate-coding in spinal motoneurons was studied using high-frequency magnetic stimulation of the human motor cortex. The subject made a weak contraction to cause rhythmic (i.e., tonic) discharge of a single motor unit in flexor (or extensor) carpi radialis or tibialis anterior, while the motor cortical representation of that muscle was stimulated with brief trains of pulses from a Pyramid stimulator (4 Magstim units connected by 3 BiStim modules). An "m@n" stimulus train consisted of m number of pulses (1-4), with an interpulse interval (IPI) of n ms (1-6). Peristimulus time histograms were constructed for each stimulus condition of a given motor unit, and related to the average rectified surface electromyography (EMG) from that muscle. Surface EMG responses showed markedly more facilitation than single-pulse stimulation, with increasing numbers of pulses in the train; responses also tended to increase in magnitude for the longer IPI values (4 and 6 ms) tested. Motor-unit response probability increased in a manner comparable to that of surface EMG. In particular, motoneurons frequently responded twice to a given stimulus train. In addition to recruitment of new motor units, the increased surface EMG responses were, in part, a direct consequence of short-term rate-coding within the tonically discharging motoneuron. Our results suggest that human corticomotoneurons are capable of reliably following high-frequency magnetic stimulation rates, and that this activity pattern is carried over to the spinal motoneuron, enabling it to discharge at extremely high rates for brief periods of time, a pattern known to be optimal for force generation at the onset of a muscle contraction.
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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".