Endocannabinoids inhibit contextual fear memory generalization via hippocampal GABAergic synaptic transmission
Bibliographic record
Abstract
Memory generalization allows an organism to adapt to new conditions, but overgeneralization of fear or traumatic experiences can be detrimental to survival and contributes to the development of various mental disorders. However, the cellular and molecular mechanisms underlying fear memory generalization, especially in the hippocampus, remain largely unknown. In this study, utilizing a well-established mouse model of fear memory generalization, we investigated the role of endocannabinoids (eCBs)-mediated GABAergic synaptic inputs to hippocampal pyramidal neurons in regulating contextual fear memory generalization. Our results revealed that pharmacological or genetic blockade of CB1R in hippocampal CA1 resulted in overgeneralization of contextual fear memory but not fear memory expression. Subsequent investigations in conditional knockout mice revealed the involvement of CB1R in GABAergic neurons, but not those in glutamatergic neurons or astrocytes, in this overgeneralization. In addition, activation of GABA A receptors on pyramidal neurons was required for inducing overgeneralization via AM281, a CB1R antagonist. Neural mechanistic studies showed that eCBs/CB1R signaling regulates both the activity and plasticity of inhibitory synapses during generalization, highlighting the prominence of the disinhibition of CB1R in interneurons during this process. Subsequently, we delved into the downstream effects and found that eCB-dependent long-term potentiation (LTP) in CA1 pyramidal neurons was regulated by the aforementioned mechanisms. Our findings illustrate that the eCBs/CB1R signaling pathway modulates the balance between fear memory discrimination and generalization by controlling inhibitory inputs to hippocampal pyramidal neurons, accompanied by alterations in excitatory plasticity within this region.
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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 | 0.000 | 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 | 0.001 |
| 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".