Neural Processing in Primary Motor Cortex and its Relationship to Contralateral and Ipsilateral Motor Actions
Bibliographic record
Abstract
The role of primary motor cortex (M1) in controlling movements has been a long-debated subject because M1 neurons have been shown to relate to many different aspects of motor control. A confounding issue is that different features of movement are inter-correlated so M1 activity related to one aspect of movement will correlate with other aspects of movement. In our first experiment, we circumvented this problem by recording from M1 neurons in monkeys across two different tasks with different kinematics and kinetics. In the first task, we applied forces to the arms in a postural task, and classified neurons into muscle-like groups based on their force preference. In the second task, the monkey reached to spatial targets and we found that the activity of each group of M1 neurons predicted the reaching directional preferences and activity patterns of their associated muscles. This suggests that M1 contributes to producing these patterns of muscle activity. The last projects explored how M1 activity can reflect information unrelated to motor patterns for the contralateral limb, such as motor patterns of the ipsilateral limb. Why does this ipsilateral-related activity not lead to contralateral limb motion? In our second experiment, we recorded from M1 neurons while we applied forces to each arm separately. We found that the torque preferences of M1 neurons were commonly different for ipsilateral and contralateral torque. The result is that M1 activity when generating ipsilateral shoulder flexor activity, would not lead to any increase in activity of any muscle group in the contralateral limb. In our third experiment, we recorded from M1 neurons while applying contralateral and ipsilateral forces at the same time. The resultant activity in M1 could be predicted by the response of the neuron during contralateral or ipsilateral torques alone, albeit scaled down in magnitude particularly related to the ipsilateral limb. The results of this thesis show that M1 is capable of producing patterns of muscle activity during voluntary motor tasks of the contralateral limb, but that other information can be simultaneously represented across the neural population.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".