Intrinsic electrical activity in gonadotropin-releasing hormone neurons: a modelling study
Bibliographic record
Abstract
Gonadotropin-releasing hormone (GnRH) neurons are neurosecretory cells of the vertebrate diencephalon that regulate fertility through pulsatile secretion of GnRH into the median eminence. The physiological mechanism for pulsatile release of GnRH is hypothesized to depend on the intrinsic electrical activity of these cells, which in mice includes two endogenous modes of action potential burst firing; namely parabolic and irregular bursting. In this thesis, we develop a stochastic Hodgkin-Huxley-like model of the electrical activity in a single GnRH neuron and use it to (i) predict the contributions of specific ionic currents in the generation of parabolic and irregular bursting, and (ii) investigate the mathematical mechanisms underlying bursting behaviour. As part of the model development process, we obtain new data-based submodels for several ionic currents that have been pharmacologically isolated in GnRH neurons. Through numerical simulations, we find that the model generates parabolic and irregular bursting solutions that agree qualitatively with electrophysiological recordings. We show that the type of bursting generated by the model can be toggled by changes in the conductances of certain ionic currents, notably those of a slow inward Ca2+current and a Ca2+-activated K+ current. The parabolic and irregular bursting models are analyzed using numerical bifurcation techniques, revealing that the two models actually share a common topological structure in their fast subsystems. Despite this mathematical similarity, the two models differ in one major aspect: parabolic bursting is not dependent on neuronal noise, whereas irregular bursting relies on slow stochastic fluctuations that push the system past the threshold for firing. Lastly, we demonstrate that a canonical model for bursting, where spiking is initiated and terminated through passage of a saddle-node on invariant circle bifurcation in the fast subsystem, can also produce both types of bursting.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".