Episodic rhythmicity is generated by a distributed neural network in the developing mammalian spinal cord
Bibliographic record
Abstract
Summary Spinal circuits produce diverse motor outputs that coordinate the rhythm and pattern of locomotor movements. Despite the episodic nature of these behaviours, the neural mechanisms encoding these episodes are not well understood. This study investigated mechanisms producing episodic rhythms evoked by dopamine in isolated neonatal mouse spinal cords. Dopamine-induced rhythms were primarily synchronous and propagated rostro-caudally across spinal segments, with occasional asynchronous episodes. Electrical stimulation of the L5 dorsal root could entrain episodes across segments, indicating afferent control of the rhythm generator and a distributed rostro-caudal network. Episodic activity was observed in isolated thoracic or sacral segments after full spinal transection or bilateral ventrolateral funiculus (VLF) lesions, suggesting a distributed network coupled via VLF projections. Rhythmicity was recorded from axons projecting through the VLF and dorsal roots, but not from cholinergic recurrent excitation via motoneurons or isolated dorsal inhibitory circuits. The data suggest episodic rhythmicity is generated by a flexibly coupled network of spinal interneurons distributed throughout the spinal cord.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".