Modulation of Rhythmic Activity in Mammalian Spinal Networks Is Dependent on Excitability State
Bibliographic record
Abstract
Neuromodulators play an important role in activating rhythmically active motor networks; however, what remains unclear are the network interactions whereby neuromodulators recruit spinal motor networks to produce rhythmic activity. Evidence from invertebrate systems has demonstrated that the effect of neuromodulators depends on the pre-existing state of the network. We explored how network excitation state affects the ability of dopamine to evoke rhythmic locomotor activity in the neonatal mouse isolated spinal cord. We found that dopamine can evoke unique patterns of motor activity that are dependent on the excitability state of motor networks. Different patterns of motor activity ranging from tonic, nonrhythmic activity to multirhythmic, nonlocomotor activity to locomotor activity were produced by altering global motor network excitability through manipulations of the extracellular potassium and bath NMDA concentration. A similar effect was observed when network excitation was manipulated during an unstable multirhythm evoked by a low concentration (15 µm) of 5-HT, suggesting that our results are not neuromodulator specific. Our data show in vertebrate systems that modulation is a two-way street and that modulatory actions are largely influenced by the network state. The level of network excitation can account for variability between preparations and is an additional factor to be considered when circuit elements are removed from the network.
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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) | 0.000 | 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) | 0.001 | 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".