On the Role of Calcium-Permeable AMPARs in Long-Term Potentiation and Synaptic Tagging in the Rodent Hippocampus
Bibliographic record
Abstract
Classically, long-term potentiation (LTP) at hippocampal CA1 synapses is triggered by the synaptic activation of NMDA receptors (NMDARs). More recently, it has been shown that calcium-permeable (CP)-AMPARs can also trigger synaptic plasticity at these synapses. Specifically, their activation is required for the PKA and protein synthesis dependent component of LTP that is typically induced by delivery of spaced trains of high frequency stimulation. Here we present new data that build upon these ideas, including the requirement for low frequency synaptic activation and NMDAR dependence. We also show that a spaced theta burst stimulation (sTBS) protocol induces a heterosynaptic potentiation of baseline responses via activation of CP-AMPARs. Finally, we present data that implicate CP-AMPARs in synaptic tagging and capture, a fundamental process that is associated with the protein synthesis-dependent component of LTP. We have studied how a sTBS can augment the level of LTP generated by a weak TBS (wTBS), delivered 30 min later to an independent input. We show that inhibition of CP-AMPARs during the sTBS eliminates, and that inhibition of CP-AMPARs during the wTBS reduces, this facilitation of LTP. These data suggest that CP-AMPARs are crucial for the protein synthesis-dependent component of LTP and its heterosynaptic nature.
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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 | 0.001 |
| Open science | 0.001 | 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".