Central Pattern Generator for Locomotion: Anatomical, Physiological, and Pathophysiological Considerations
Bibliographic record
Abstract
This article provides a perspective on major innovations over the past century in research on the spinal cord and, specifically, on specialized spinal circuits involved in the control of rhythmic locomotor pattern generation and modulation. Pioneers such as Charles Sherrington and Thomas Graham Brown have conducted experiments in the early twentieth century that changed our views of the neural control of locomotion. Their seminal work supported subsequently by several decades of evidence has led to the conclusion that walking, flying, and swimming are largely controlled by a network of spinal neurons generally referred to as the central pattern generator (CPG) for locomotion. It has been subsequently demonstrated across all vertebrate species examined, from lampreys to humans, that this CPG is capable, under some conditions, to self-produce, even in absence of descending or peripheral inputs, basic rhythmic, and coordinated locomotor movements. Recent evidence suggests, in turn, that plasticity changes of some CPG elements may contribute to the development of specific pathophysiological conditions associated with impaired locomotion or spontaneous locomotor-like movements. This article constitutes a comprehensive review summarizing key findings on the CPG as well as on its potential role in Restless Leg Syndrome, Periodic Leg Movement, and Alternating Leg Muscle Activation. Special attention will be paid to the role of the CPG in a recently identified, and uniquely different neurological disorder, called the Uner Tan Syndrome.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".