A transient neural code for feedback-driven motor corrections during reaching
Bibliographic record
Abstract
Neural activity in motor cortical areas is well-explained by latent neural population dynamics: the motor preparation phase sets the initial condition for the movement while the dynamics that unfold during the motor execution phase orchestrate the sequence of muscle activations. While preparatory activity explains a large fraction of both neural and behavior variability during the execution of a planned movement, it cannot account for corrections and adjustments during movements as this requires sensory feedback not available during planning. Therefore, accounting for unplanned, sensory-guided movement requires knowledge of relevant inputs to the motor cortex from other brain areas. Here, we provide evidence that these inputs cause transient deviations from an autonomous neural population trajectory, and show that these dynamics cannot be found by unsupervised inference methods. We introduce the new Behavior-Aligned Neural Dynamics (BAND) model, which exploits semi-supervised learning to predict both planned and unplanned movements from neural activity in the motor cortex that can be missed by unsupervised inference methods. Our analysis using BAND suggests that 1) transient motor corrections are encoded in small neural variability; 2) motor corrections are encoded in a sparse sub-population of primary motor cortex neurons (M1); and 3) combining latent dynamical modeling with behavior supervision allows for capturing both the movement plan and corrections.
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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 | 0.002 |
| 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 | 0.001 |
| 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".