A model of the basal ganglia in voluntary movement and postural reactions
Bibliographic record
Abstract
A basal ganglia central pattern generator (CPG) is developed and its role in voluntary movements on the ground and postural reactions on a disturbed platform are studied and analysed by simulation. Biped dynamics and platform kinematics are utilised. The effects of agonist-antagonist muscular co-activation and joint stiffness are formulated. The implementation of the necessary counter-manoeuvres for maintaining balance and postural stability is studied. A control strategy, applicable to large systems, is formulated. The biped manoeuvres and transitions terminate in pre-specified intervals of time. Gravity is included and compensated for. Certain voluntary and postural adjustment strategies are the same but are initiated differently. Further experimental/computational research may identify the central nervous system and sensory paths that lead to the CPG. All actuator forces linearly evolve in time from their original values to their terminal values. There are no central continuous feedback loops present. Monitoring and sensing, however, are ongoing. The counter-manoeuvres are based on learned human-like voluntary movements that are triggered by the disturbance. The required central inputs to the musculoskeletal system are designed in the CPG. A functional structure for the CPG is proposed. The effect of certain disorders and malfunctions of the CPG are studied by simulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".