Particle-Swarm Based Modelling Reveals Two Distinct Classes of CRH <sup>PVN</sup> Neurons
Bibliographic record
Abstract
Abstract Electrophysiological recordings can provide detailed information of single neurons’ dynamical features and shed light into their response to stimuli. Unfortunately, rapidly modeling electrophysiological data for inferring network-level behaviours remains challenging. Here, we investigate how modeled single neuron dynamics lead to network-level responses in the paraventricular nucleus of the hypothalamus (PVN), a critical nucleus for the mammalian stress response. Recordings of corticotropinreleasing hormone neurons from the PVN (CRH PVN ) were performed using whole-cell current-clamp. These, neurons, which initiate the endocrine response to stress, were rapidly and automatically fit to a modified Adaptive Exponential Integrate and Fire model (AdEx) with Particle Swarm Optimization (PSO). All CRH PVN neurons were accurately fit by the AdEx model with PSO. Multiple sets of parameters were found that reliably reproduced current-clamp traces for any single neuron. Despite multiple solutions, the dynamical features of the models such as the rheobase current levels, fixed points, and bifurcations, were shown to be stable across fits. We found that CRH PVN neurons can be divided into two sub-types according to their bifurcation at the onset of firing: saddles (integrators) and sub-critical Hopf (resonators). We constructed networks of these fit CRH PVN model neurons to investigate the network level responses of CRH PVN neurons. We found that CRH PVN -resonators maintain baseline firing in networks even when all inputs are inhibitory. The dynamics of a small subset of CRH PVN neurons may be critical to maintaining a baseline firing tone in the PVN. Key Points Corticotropin-releasing hormone neurons (CRH PVN ) in the paraventricular nucleus of the hypothalamus act as the final neural controllers of the stress response. We developed a rapid computational modeling platform that uses Particle-Swarm Optimization to rapidly and accurately fit biophysical neuron models. A model was fit to each patched neuron without the use of dynamic clamping, or other procedures requiring sophisticated inputs and fitting procedures. Any neuron undergoing standard current clamping for a few minutes can be fit by this procedure The dynamical analysis of the modeled neurons shows thatCRH PVN comes in two specific ‘flavours’: CRH PVN -resonators and CRH PVN -integrators. Network simulations show thatCRH PVN -resonators are critical to retaining the baseline firing rate of the entire network of CRH PVN neurons as these cells can fire rebound spikes and bursts in the presence of strong inhibitory synaptic input.
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) | 0.000 | 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) | 0.001 | 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".