Bibliographic record
Abstract
Motor cortex is the principal driver of discrete, voluntary movements like reaching. Correspondingly, current theories describe muscle activity as a function of cortical dynamics. Tasks like speech and locomotion, however, require the integration of voluntary commands with ongoing movements orchestrated by largely independent subcortical centers. In such cases, motor cortex must receive inputs representing the state of the environment and the state of subcortical networks, then transform these inputs into commands that modulate the rhythmic motor pattern. Here, we study this transformation in mice performing an obstacle traversal task, which combines a spinal locomotor pattern with voluntary cortical adjustments. Cortical dynamics contain a prominent representation of motor preparation that is linked to obstacle proximity and robust to removal of somatosensory or visual input, and also maintain a representation of the state of the spinal pattern generator. Readout signals resembling commands for obstacle traversal are consistent across trials, but small in amplitude. Using computational modeling, we identify a simple algorithm that generates the appropriate commands through phase-dependent gating. Together, these results reveal a regime in which motor cortex does not fully specify muscle activity, but must sculpt an ongoing, spinally-generated program to flexibly control behavior in a complex and changing environment.
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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 | 0.000 |
| 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) | 0.002 | 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".