Mechanisms underlying the noradrenergic modulation of longitudinal coordination during swimming in <i>Xenopus laevis</i> tadpoles
Bibliographic record
Abstract
Noradrenaline (NA) is a potent modulator of locomotion in many vertebrate nervous systems. When Xenopus tadpoles swim, waves of motor neuron activity alternate across the body and propagate along it with a brief rostro-caudal delay (RC-delay) between segments. We have now investigated the mechanisms underlying the reduction of RC-delay s by NA. When recording from motor neurons caudal to the twelfth postotic cleft, the mid-cycle inhibition was weak and sometimes absent, compared to more rostral locations. NA enhanced and even unmasked inhibition in these caudal neurons and enhanced inhibition in rostral neurons, but to a lesser extent. Consequently, the relative increase in the amplitude of the inhibition was greater in caudal neurons, thus reducing the RC-inhibitory gradient. We next investigated whether NA might affect the electrical properties of neurons, such that enhanced inhibition under NA might promote postinhibitory rebound firing. The synaptic inputs during swimming were simulated using a sustained positive current, superimposed upon which were brief negative currents. When these conditions were held constant NA enhanced the probability of rebound firing--indicating a direct effect on membrane properties--in addition to any indirect effect of enhanced inhibition. We propose that NA preferentially enhances weak caudal inhibition, reducing the inhibitory gradient along the cord. This effect on inhibitory synaptic transmission, comprising parallel pre- and postsynaptic components, will preferentially facilitate rebound firing in caudal neurons, advancing their firing relative to more rostral neurons, whilst additionally increasing the networks ability to sustain the longer cycle periods under NA.
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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".