Neural Activity in Primary Motor Cortex Related to Mechanical Loads Applied to the Shoulder and Elbow During a Postural Task
Bibliographic record
Abstract
Whole-arm motor tasks performed by nonhuman primates have become a popular paradigm to examine neural activity during motor action, but such studies have traditionally related cell discharge to hand-based variables. We have developed a new robotic device that allows the mechanics of the shoulder and elbow joints to be manipulated independently. This device was used in the present study to examine neural activity in primary motor cortex (MI) in monkeys (Macaca mulatta) actively maintaining their hand at a central target as they compensated for loads applied to the shoulder and/or elbow. Roughly equal numbers of neurons were sensitive to mechanical loads only at the shoulder, only at the elbow, or loads at both joints. Neurons possessed two important properties. First, cell activity during multi-joint loads could be predicted from its activity during single-joint loads as a vector sum in a space defined by orthogonal axes for the shoulder and elbow. Second, most neurons were related to flexor torque at one joint coupled with extensor torque at the other, a distribution that paralleled the observed activity of forelimb muscles. These results illustrate that while MI activity may be described by independent axes representing each mechanical degree-of-freedom, neural activity is also strongly influenced by the specific motor patterns used to perform a given task.
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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".