Neuromodulation of Spinal Locomotor Networks in Rodents
Bibliographic record
Abstract
BACKGROUND: The basic motor patterns driving rhythmic limb movements during walking are generated by networks of neurons called central pattern generators (CPGs). Within motor control systems, neuromodulators are necessary for proper and efficient CPG function because they induce or regulate essential components of spinal network activity, including firing parameters of CPG neurons and network synaptic strength, allowing the network to change/adapt and sometimes to even become functional. METHODS: The goal of this work is to focus on classical and recent findings addressing the role of neuromodulators such as glutamate, dopamine, acetylcholine and adenosine in eliciting, changing and sometimes terminating spinal CPG network function in rodents. RESULTS: Neuromodulatory inputs onto CPG locomotor networks have been additionally related to inducing state changes such as locomotor timing, phasing and speed, and to the induction/maintenance of actual network function. These inputs originate from supraspinal centers such as the brainstem and from intraspinal neurotransmission. The isolated in vitro rodent spinal cord preparation is a powerful model for studies on locomotor network organization because of its physiological and anatomical accessibility, as well as the incorporation of various transgenic approaches to identify specific neuronal populations. Both roles are accomplished through the action of neuromodulators on ionotropic and metabotropic receptors mediating synaptic neurotransmission, which can be used by neurons that are intrinsic or extrinsic components of a CPG network itself. CONCLUSION: This article has hopefully provided a comprehensive overview of some of the main spinal mechanisms involved in the modulatory control of locomotor activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".