Glutamate dynamics determine the magnitude of Hebbian synaptic plasticity
Bibliographic record
Abstract
Increasing evidence suggests that synaptic NMDA receptors (NMDARs) promote long term potentiation (LTP) while extrasynaptic NMDARs inhibit LTP and promote long term depression (LTD). Glutamate transporters maintain this balance by rapidly clearing glutamate from the extracellular space. In many disease states, transporter dysfunction is thought to underlie LTP deficits. However, the precise relationship between extracellular glutamate dynamics and LTP is unknown. Here, we used an optogenetic sensor of glutamate to monitor glutamate dynamics in real-time during LTP induction. Pharmacologically blocking glutamate transporters slowed clearance and inhibited LTP magnitude in a concentration-dependent manner. Surprisingly, impaired glutamate clearance caused rapid NMDAR desensitization and simultaneous three-fold increases in postsynaptic calcium through L-type voltage gated calcium channels. Overall, our data characterize the relationship between glutamate dynamics and LTP, and identify a novel mechanism underlying LTP impairment due to slow glutamate clearance. These results may be applicable to neurodegenerative diseases associated with impaired synaptic plasticity and glutamate transporter dysfunction.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".