Serotonin patterns locomotor network activity in the developing zebrafish by modulating quiescent periods
Bibliographic record
Abstract
Developing neural networks follow common trends such as expression of spontaneous, recurring activity patterns, and appearance of neuromodulation. How these processes integrate to yield mature, behaviorally relevant activity patterns is largely unknown. We examined the integration of serotonergic neuromodulation and its role in the functional organization of the accessible locomotor network in developing zebrafish at behavioral and cellular levels. Locally restricted populations of serotonergic neurons and their projections appeared in the hindbrain and spinal cord of larvae after hatching (approximately day 2). However, 5-HT affected the swimming pattern only from day 4 on, when sustained spontaneous swimming appeared. 5-HT and its agonist quipazine increased motor output by reducing intervals of inactivity, observed behaviorally (by high-speed video) and in recordings from spinal neurons during fictive swimming (by whole-cell current clamp). 5-HT and quipazine had little effect on the properties of the activity periods, such as the duration of swim episodes and swim frequency. Further, neuronal input resistance, rheobasic current, and resting potential were not affected significantly. The 5-HT antagonists methysergide and ketanserin decreased motor output by prolonging the periods of inactivity with little effect on the active swim episode or neuronal properties. Our results suggest that 5-HT neuromodulation is integrated early in development of the locomotor network to increase its output by reducing periods of inactivity with little effect on the activity periods, which in contrast are the main targets of 5-HT neuromodulation in neonatal and adult preparations.
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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".