The Human Central Pattern Generator for Locomotion: Does It Exist and Contribute to Walking?
Bibliographic record
Abstract
The ability of dedicated spinal circuits, referred to as central pattern generators (CPGs), to produce the basic rhythm and neural activation patterns underlying locomotion can be demonstrated under specific experimental conditions in reduced animal preparations. The existence of CPGs in humans is a matter of debate. Equally elusive is the contribution of CPGs to normal bipedal locomotion. To address these points, we focus on human studies that utilized spinal cord stimulation or pharmacological neuromodulation to generate rhythmic activity in individuals with spinal cord injury, and on neuromechanical modeling of human locomotion. In the absence of volitional motor control and step-specific sensory feedback, the human lumbar spinal cord can produce rhythmic muscle activation patterns that closely resemble CPG-induced neural activity of the isolated animal spinal cord. In this sense, CPGs in humans can be defined by the activity they produce. During normal locomotion, CPGs could contribute to the activation patterns during specific phases of the step cycle and simplify supraspinal control of step cycle frequency as a feedforward component to achieve a targeted speed. Determining how the human CPGs operate will be essential to advance the theory of neural control of locomotion and develop new locomotor neurorehabilitation paradigms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".