Widespread innervation of motoneurons by spinal V3 neurons globally amplifies locomotor output in mice
Bibliographic record
Abstract
Abstract While considerable progress has been made in understanding the neuronal circuits that underlie the patterning of locomotor behaviours such as walking, less is known about the circuits that amplify motoneuron output to enable adaptable increases in muscle force across different locomotor intensities. Here, we demonstrate that an excitatory propriospinal neuron population (V3 neurons, Sim1 + ) forms a large part of the total excitatory interneuron input to motoneurons (∼20%) across all hindlimb muscles. Additionally, V3 neurons make extensive connections among themselves and with other excitatory premotor neurons (such as V2a neurons). These circuits allow local activation of V3 neurons at just one segment (via optogenetics) to rapidly depolarize and amplify locomotor-related motoneuron output at all lumbar segments in both the in vitro spinal cord and the awake adult mouse. Interestingly, despite similar innervation from V3 neurons to flexor and extensor motoneuron pools, functionally, V3 neurons exhibit a pronounced bias towards activating extensor muscles. Furthermore, the V3 neurons appear essential to extensor activity during locomotion because genetically silencing them leads to slower and weaker mice with a poor ability to increase force with locomotor intensity, without much change in the timing of locomotion. Overall, V3 neurons increase the excitability of motoneurons and premotor neurons, thereby serving as global command neurons that amplify the locomotion intensity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".