Synapse-Specific Regulation of AMPA Receptor Subunit Composition by Activity
Bibliographic record
Abstract
We examined the changes that arise when neurotransmitter release is inhibited in a subpopulation of hippocampal neurons in coculture with normally active neighbors. Subsets of neurons were presynaptically silenced by chronic expression of tetanus toxin light chain tagged with cyan fluorescent protein (TNTCFP). Surprisingly, silenced neurons formed as many presynaptic terminals as their active neighbors when grown together on glial microislands. However, silenced neurons could not recruit the AMPA-type glutamate receptor subunit GluR1 as efficiently when competing with active neighbors. The immunofluorescence intensity ratio of GluR1 at synaptic puncta versus shaft was reduced by 22% opposite TNTCFP-expressing terminals compared with active neighbors. In contrast, this effect is abolished when vesicular release is blocked in all neurons. Local presynaptic inhibition by TNTCFP did not change the synaptic level of the AMPA receptor subunits GluR2 or GluR2/3 or of the PSD95 (postsynaptic density 95) family scaffolding proteins. Thus, neurotransmitter release selectively regulates the AMPA receptor population on a synapse-by-synapse basis but is not essential for an axon to efficiently compete for synaptic territory in a simple model system. These results demonstrate precise input specificity of postsynaptic receptor composition via differential activity among neighbor 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 | 0.000 |
| 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".