Emergence of Deletions during Treadmill Locomotion as a Function of Supraspinal and Sensory Inputs
Bibliographic record
Abstract
During locomotion, alternating activity of flexor and extensor muscles is largely regulated by a spinal neuronal network, the central pattern generator, the activity of which is modulated by peripheral and supraspinal inputs. In the absence of these modulatory inputs, for example during fictive locomotion after spinalization and curarization, spontaneous failures of motor activation (deletions) in a muscle can occur without perturbing the rhythmic cycle structure of the antagonists on the same side or the contralateral side. This suggests that the central pattern generator can maintain the locomotor period when motoneuron discharges fail in a given pool of motoneurons. Here we first examined whether such deletions could occur during real locomotion on a treadmill and determined their consequences on the overt locomotor pattern. We also evaluated the role of supraspinal and sensory inputs in modulating the occurrence of failures of rhythmic activity by comparing the same cats in the intact state, then after a partial spinal cord injury (SCI), and finally after a complete SCI at different treadmill speeds. We showed that deletions: (1) are absent in intact animals and occur only after SCI; (2) affect only flexor muscle activity; (3) neither perturb the timing of rhythmic activity of these muscles in subsequent cycles nor interfere with the timing of the ipsilateral and contralateral agonists and antagonists; (4) do not affect significantly the locomotor pattern kinematics; and (5) are sensitive to treadmill speed and lesion severity, suggesting a role for sensory and supraspinal inputs in stabilizing rhythmic output activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".