Inhibitory circuits in fear memory and fear-related disorders
Bibliographic record
Abstract
Fear learning and memory rely on dynamic interactions between the excitatory and inhibitory neuronal populations that make up the prefrontal cortical, amygdala, and hippocampal circuits. Whereas inhibition of excitatory principal cells (PCs) by GABAergic neurons restrains their excitation, inhibition of GABAergic neurons promotes the excitation of PCs through a process called disinhibition. Specifically, GABAergic interneurons that express parvalbumin (PV+) and somatostatin (SOM+) provide inhibition to different subcellular domains of PCs, whereas those that express the vasoactive intestinal polypeptide (VIP+) facilitate disinhibition of PCs by inhibiting PV+ and SOM+ interneurons. Importantly, although the main connectivity motifs and the underlying network functions of PV+, SOM+, and VIP+ interneurons are replicated across cortical and limbic areas, these inhibitory populations play region-specific roles in fear learning and memory. Here, we provide an overview of the fear processing in the amygdala, hippocampus, and prefrontal cortex based on the evidence obtained in human and animal studies. Moreover, focusing on recent findings obtained using genetically defined imaging and intervention strategies, we discuss the population-specific functions of PV+, SOM+, and VIP+ interneurons in fear circuits. Last, we review current insights that integrate the region-specific inhibitory and disinhibitory network patterns into fear memory acquisition and fear-related disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".