Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion
Bibliographic record
Abstract
The mammalian spinal cord contains a locomotor central pattern generator (CPG) that can produce alternating rhythmic activity of flexor and extensor motoneurones in the absence of rhythmic input and proprioceptive feedback. During such fictive locomotor activity in decerebrate cats, spontaneous omissions of activity occur simultaneously in multiple agonist motoneurone pools for a number of cycles. During these 'deletions', antagonist motoneurone pools usually become tonically active but may also continue to be rhythmic. The rhythmic activity that re-emerges following a deletion is often not phase shifted. This suggests that some neuronal mechanism can maintain the locomotor period when motoneurone activity fails. To account for these observations, a simplified computational model of the spinal circuitry has been developed in which the locomotor CPG consists of two levels: a half-centre rhythm generator (RG) and a pattern formation (PF) network, with reciprocal inhibitory interactions between antagonist neural populations at each level. The model represents a network of interacting neural populations with single interneurones and motoneurones described in the Hodgkin-Huxley style. The model reproduces the range of locomotor periods and phase durations observed during real locomotion in adult cats and permits independent control of the level of motoneurone activity and of step cycle timing. By altering the excitability of neural populations within the PF network, the model can reproduce deletions in which motoneurone activity fails but the phase of locomotor oscillations is maintained. The model also suggests criteria for the functional identification of spinal interneurones involved in the mammalian locomotor pattern generation.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".