GABAergic Network Activation of Glial Cells Underlies Hippocampal Heterosynaptic Depression
Bibliographic record
Abstract
Tetanus-induced heterosynaptic depression in the hippocampus is a key cellular mechanism in neural networks implicated in learning and memory. A growing body of evidence indicates that glial cells are important modulators of synaptic functions, but very little is known about their role in heterosynaptic plasticity. We examined the role of glial cells in heterosynaptic depression, knowing that tetanization and NMDA application caused depression of synaptic field responses (fEPSPs) and induced Ca2+ rise in glial cells. Here we report that chelating Ca2+ in a glial syncytium interfered with heterosynaptic depression and NMDA-induced fEPSP depression, suggesting that Ca2+ activation of glial cells is necessary for heterosynaptic depression. The NMDA-induced Ca2+ rise in glial cells was sensitive to tetrodotoxin and reduced by the GABAB antagonist CGP55845. Both heterosynaptic depression and simultaneous Ca2+ activation of glial cells were prevented by CGP55845, suggesting an involvement of the GABAergic network in glial activation and heterosynaptic depression. Also, the GABAB agonist baclofen caused both a Ca2+ rise in glial cells and fEPSP depression. Heterosynaptic depression, as well as NMDA- and baclofen-induced depression, were attenuated by an A1 antagonist, cyclopentyl-theophylline, whereas glial cell activation was not, indicating a role of adenosine downstream of glial activation. Finally, heterosynaptic depression requires ATP degradation because ectonucleotidase inhibitors reduced this plasticity. Our work indicates that Ca2+ activation of glial cells is necessary for heterosynaptic depression, which involves the sequential interaction of Schaffer collaterals, the GABAergic network, and glia. Thus, glial and neuronal networks are functionally associated during the genesis of heterosynaptic plasticity at mammalian central excitatory synapses.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".