Spinal V1 neurons inhibit motor targets locally and sensory targets distally to coordinate locomotion
Bibliographic record
Abstract
Abstract Rostro-caudal coordination of spinal motor output is essential for locomotion. Most spinal interneurons project axons longitudinally to govern locomotor output, yet their connectivity along this axis remains unclear. In this study, we use larval zebrafish to map synaptic outputs of a major inhibitory population, V1 (Eng1+) neurons, which are implicated in dual sensory and motor functions. We find that V1 neurons exhibit long axons extending rostrally and exclusively ipsilaterally for an average of 6 spinal segments; however, they do not connect uniformly with their post-synaptic targets along the entire length of their axon. Locally, V1 neurons inhibit motor neurons (both fast and slow) and other premotor targets including V2a, V2b and commissural pre-motor neurons. In contrast, V1 neurons make robust inhibitory contacts throughout the rostral extent of their axonal projections onto a dorsal horn sensory population, the Commissural Primary Ascending neurons (CoPAs). In a computational model of the ipsilateral spinal network, we show that this pattern of short range V1 inhibition to motor and premotor neurons is crucial for coordinated rostro-caudal propagation of the locomotor wave. We conclude that spinal network architecture in the longitudinal axis can vary dramatically, with differentially targeted local and distal connections, yielding important consequences for function.
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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 | 0.001 |
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".