Dynamic Sensorimotor Interactions in Locomotion
Bibliographic record
Abstract
Locomotion results from intricate dynamic interactions between a central program and feedback mechanisms. The central program relies fundamentally on a genetically determined spinal circuitry (central pattern generator) capable of generating the basic locomotor pattern and on various descending pathways that can trigger, stop, and steer locomotion. The feedback originates from muscles and skin afferents as well as from special senses (vision, audition, vestibular) and dynamically adapts the locomotor pattern to the requirements of the environment. The dynamic interactions are ensured by modulating transmission in locomotor pathways in a state- and phase-dependent manner. For instance, proprioceptive inputs from extensors can, during stance, adjust the timing and amplitude of muscle activities of the limbs to the speed of locomotion but be silenced during the opposite phase of the cycle. Similarly, skin afferents participate predominantly in the correction of limb and foot placement during stance on uneven terrain, but skin stimuli can evoke different types of responses depending on when they occur within the step cycle. Similarly, stimulation of descending pathways may affect the locomotor pattern in only certain phases of the step cycle. Section ii reviews dynamic sensorimotor interactions mainly through spinal pathways. Section iii describes how similar sensory inputs from the spinal or supraspinal levels can modify locomotion through descending pathways. The sensorimotor interactions occur obviously at several levels of the nervous system. Section iv summarizes presynaptic, interneuronal, and motoneuronal mechanisms that are common at these various levels. Together these mechanisms contribute to the continuous dynamic adjustment of sensorimotor interactions, ensuring that the central program and feedback mechanisms are congruous during locomotion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".