Hypothalamic recurrent inhibition regulates functional states of stress effector neurons
Bibliographic record
Abstract
ABSTRACT Stress triggers rapid and reversible shifts in vital physiological functions from homeostatic operation to emergency response. However, the neural mechanisms regulating such functional stress states remain poorly understood. Here we identify a novel recurrent inhibitory circuit governing functional states of key stress regulatory neurons: corticotropin-releasing hormone (CRH) neurons in the hypothalamic paraventricular nucleus (PVN). Microendoscopic calcium imaging in freely behaving mice revealed synchronized low-activity state at baselines and a reversible high-activity state during mild stress. Ensemble analysis indicated increased dimensionality of network dynamics during high-activity state. Computational modeling of calcium ensemble data, together with independent modeling of single-unit CRH PVN neurons spiking dynamics, converged to show that recurrent inhibition is a key circuit motif for stress-induced functional state transitions. Guided by model predictions, chemogenetic manipulations of PVN-projecting GABAergic neurons ( PVN→ GABA) revealed their roles in constraining CRH PVN neurons to low-activity state at baselines via a prolonged feedback inhibition. Unexpectedly, slow CRHergic excitation was dispensable for driving this prolonged feedback, whereas glutamatergic transmission predominated at CRH→ PVN→ GABA excitatory synapses. Incorporating these findings, we refined our computational model to include fast excitation and slow inhibition, yielding new predictions for circuit operation. Together, our results establish recurrent inhibition as a fundamental circuit motif controlling CRH PVN neurons functional states and highlight the value of iterative experiment–model integration in advancing understanding of neural circuits functions.
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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) | 0.001 | 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".