Modeling Subfornical Organ Neurons
Bibliographic record
Abstract
Subfornical organ (SFO) neurons exhibit heterogeneity in ionic current expression and spiking behaviour, where the two major 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 autonomic nervous system, endocrine, and behavioural function. To integrate efficient methods for investigating this heterogeneity, we built a single-compartment, Hodgkin-Huxley type model of an SFO neuron that is parameterized by SFO-specific in vitro voltage-clamp data. The model accounts for the individual membrane potential distribution and spike train variability of tonic and burst firing SFO neurons. Analysis of model dynamics confirms that a persistent Na+ and a Ca2+ current 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 underlies the behavioural differences between tonic and burst firing SFO neurons. The use of our model in coordination with in vitro electrophysiology experiments, provides a platform for explaining and predicting the response of SFO neurons to various combinations of circulating signals, as demonstrated by our preliminary investigation of inflammatory and cardiovascular signal integration. Our model predicts that 24-hr incubation in tumor necrosis factor alpha, an inflammatory cytokine, will result in the potentiation of SFO neuron excitability in response to angiotensin II. This prediction provides a potential mechanism to support previous findings that inflammation may be potentiating angiotensin II actions in the SFO. Future studies will work to further elucidate the mechanisms underlying the integration of physiologically important signals in the SFO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".