Homeostatic scaling ensures behavioural stability during corticosterone negative feedback
Bibliographic record
Abstract
Maintaining appropriate behavioral and physiological responses in the face of challenge is essential for survival. The persistent increase in corticosteroids (CORT) during chronic stress blunts the endocrine response to any subsequent stressors. But the impact of prolonged CORT on behaviors that promote survival in the face of an acute stress is not well understood. Here we used an aerial predator threat model combined with in vivo calcium imaging, whole-cell electrophysiology, chemogenetics and computational modeling to evaluate the effects of short and long-term CORT. We show that in the short term, the activity of the corticotropin releasing hormone neurons of the paraventricular nucleus of the hypothalamus (CRHPVN) and innate defensive behaviors that rely on these cells, are sensitive to the negative feedback effects of CORT. In response to long-term increases in CORT, however, behaviors recover, even though intrinsic CRHPVN activity remains low. This escape from negative feedback requires local, homeostatic scaling of glutamate synapses that overcomes the inhibitory effects of CORT. This scaling is sufficient to maintain the output of this system in vivo and preserves innate defensive responses to threat. We propose that homeostatic synaptic scaling functions as a local adaptive mechanism to preserve the reliability of essential survival circuits during times of chronic stress.
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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".