Ionic mechanisms underlying tonic and burst firing behavior in subfornical organ neurons: a combined experimental and modeling study
Bibliographic record
Abstract
Subfornical organ (SFO) neurons exhibit heterogeneity in current expression and spiking behavior, where the two major spiking phenotypes appear as tonic and burst firing. Insight into the mechanisms behind this heterogeneity is critical for understanding how the SFO, a sensory circumventricular organ, integrates and selectively influences physiological function. To integrate efficient methods for studying this heterogeneity, we built a single-compartment, Hodgkin-Huxley-type model of an SFO neuron that is parameterized by SFO-specific in vitro patch-clamp data. The model accounts for the membrane potential distribution and spike train variability of both tonic and burst firing SFO neurons. Analysis of model dynamics confirms that a persistent Na+ and Ca2+ currents are required for burst initiation and maintenance and suggests that a slow-activating K+ current may be responsible for burst termination in SFO neurons. Additionally, the model suggests that heterogeneity in current expression and subsequent influence on spike afterpotential underlie the behavioral differences between tonic and burst firing SFO neurons. Future use of this model in coordination with single neuron patch-clamp electrophysiology provides a platform for explaining and predicting the response of SFO neurons to various combinations of circulating signals, thus elucidating the mechanisms underlying physiological signal integration within the SFO. NEW & NOTEWORTHY Our understanding of how the subfornical organ (SFO) selectively influences autonomic nervous system function remains incomplete but theoretically results from the electrical responses of SFO neurons to physiologically important signals. We have built a computational model of SFO neurons, derived from and supported by experimental data, which explains how SFO neurons produce different electrical patterns. The model provides an efficient system to theoretically and experimentally explore how changes in the essential features of SFO neurons affect their electrical activity.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".