Deciphering the organization and modulation of spinal locomotor central pattern generators
Bibliographic record
Abstract
Networks within our spinal cord generate the basic pattern underlying walking. Over the past decade, much progress has been made in our understanding of their function in a variety of vertebrate species. A significant hurdle has been the identification of candidate populations of neurons that are involved in pattern generation in the spinal cord. Recently, systems neuroscientists in collaboration with molecular biologists have begun to dissect the circuitry underlying spinal locomotor networks. These advances have combined genetic and electrophysiological techniques using in vitro preparations of the mouse spinal cord. This review will discuss new advances in the field of spinal locomotor networks with emphasis on the mouse. Many of the behaviors fundamental to animal life, such as breathing, chewing and locomotion, are rhythmic activities controlled by neuronal networks. Discerning which neurons are members of these networks, their synaptic connectivity and their individual electrophysiological properties is essential to our understanding of how rhythmic motor behaviors are produced. It is well known that the spinal cord contains the basic circuitry to produce locomotion. However, identifying neurons and connections within spinal networks is challenging because cells that comprise the locomotor network form part of a heterogeneous mix of interneurons within the ventral spinal cord. Recently, the merging of electrophysiological and genetic approaches has provided new tools to identify classes of interneurons within the spinal cord that contribute to network function. These new findings will be discussed in this review.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".