The Role of Genetically-Defined Interneurons in Generating the Mammalian Locomotor Rhythm
Bibliographic record
Abstract
Locomotor behavior in mammals requires a complex pattern of muscle activation. Neural networks, known as central pattern generators (CPGs) and located entirely within the spinal cord, are responsible for generating much of the timing and pattern required for locomotor movements. Historically, identification of interneuronal components of the locomotor CPG in walking mammals has proven troublesome, primarily because of the difficulty in identifying functionally homogeneous groups of neurons in the spinal cord. Recently, a molecular approach has been used to identify populations of genetically similar interneurons based on the expression of transcription factors early in embryonic development. Preliminary work on these cell populations has shown that many comprise essential components of the locomotor CPG. Here I identify populations of genetically-defined interneurons that are candidate "first-order" cells of this neural network, potentially responsible for generating the locomotor rhythm in the mammalian spinal cord. Identification of the cell population(s) responsible for this key function will provide valuable insight into the structure and function of the locomotor CPG and could potentially lay the groundwork for the development of strategies aimed at regenerating motor pathways following injury to the spinal cord.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".