Neuromodulators, Not Activity, Control Coordinated Expression of Ionic Currents
Bibliographic record
Abstract
Electrical activity in identical neurons across individuals is often remarkably similar and stable over long periods. However, the ionic currents that determine the electrical activity of these neurons show wide animal-to-animal amplitude variability. This seemingly random variability of individual current amplitudes may obscure mechanisms that globally reduce variability and that contribute to the generation of similar neuronal output. One such mechanism could be the coordinated regulation of ionic current expression. Studying identified neurons of the Cancer borealis pyloric network, we discovered that the removal of neuromodulatory input to this network (decentralization) was accompanied by the loss of the coordinated regulation of ionic current levels. Additionally, decentralization induced large changes in the levels of several ionic currents. The loss of coregulation and the changes in current levels were prevented by continuous exogenous application of proctolin, an endogenous neuromodulatory peptide, to the pyloric network. This peptide does not exert fast regulatory actions on any of the currents affected by decentralization. We conclude that neuromodulatory inputs to the pyloric network have a novel role in the regulation of ionic current expression. They can control, over the long term, the coordinated expression of multiple voltage-gated ionic currents that they do not acutely modulate. Our results suggest that current coregulation places constraints on neuronal intrinsic plasticity and the ability of a network to respond to perturbations. The loss of conductance coregulation may be a mechanism to facilitate the recovery of function.
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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) | 0.000 | 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".