Computational modelling of functional maturation of primary motoneuron firing properties in developing zebrafish
Bibliographic record
Abstract
ABSTRACT Several ion currents of zebrafish primary motoneurons undergo changes in expression level during early development. Similarly, the firing properties of primary motoneurons and their involvement during locomotor activity change during early development as locomotor control of developing zebrafish matures. To test whether the experimentally observed changes in ion currents during development could underlie changes in firing properties and in participation during locomotor activity, we created models of primary motoneurons at developmental stages. Changes in the expression levels of a persistent outward potassium current, persistent inward potassium current, and several high-voltage activated calcium currents were modelled based on experimental observations. Simulations of our computational models replicated shifts in primary motoneuron firing properties and involvement during light-evoked swimming observed at 3 and 5 days post-fertilization. Our results suggest that developmental changes in specific ion currents of primary motoneurons could be sufficient to foster changes in firing properties of primary motoneurons that shape their activity level during maturation of motor control in developing zebrafish. Key points Developmental changes in persistent inward and outward currents could explain changes in firing properties of primary motoneurons in larval zebrafish Modelling suggests these currents are sufficient to explain differences in primary motoneuron firing during light-evoked swimming responses at two developmental stages Developmental changes in high-voltage activated calcium currents explain differences in appearance of persistent inward currents during voltage-clamp ramp Interaction between high-voltage activated calcium currents and calcium-dependent potassium currents could explain why blocking calcium currents increases primary motoneuron firing
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".