The Stability of Glutamatergic Synapses Is Independent of Activity Level, but Predicted by Synapse Size
Bibliographic record
Abstract
Neuronal activity is thought to drive the remodeling of circuits in the mammalian cerebral cortex. However, its precise function in the underlying formation and elimination of glutamatergic synapses has remained controversial. To clarify the role of activity in synapse turnover, we have assessed the effects of inhibition of glutamate release from a sparse subset of cultured hippocampal neurons on synapse turnover. Sustained chemogenetic attenuation of release through presynaptic expression of a designer receptor exclusively activated by designer drugs (DREADD) had no effect on the formation or elimination of glutamatergic synapses. Sparse expression of tetanus neurotoxin light chain (TeNT-LC), a synaptobrevin-cleaving protease that completely abolishes neurotransmitter release, likewise did not lead to changes in the rate of synapse elimination, although it reduced the rate of synapse formation. The stability of active and silenced synapses correlated with measures of synapse size. While not excluding a modulatory role in synapse elimination, our findings show that synaptic activity is neither required for the removal nor the maintenance of glutamatergic synapses between hippocampal neurons. Our results also demonstrate that the stability of glutamatergic synapses scales with their size irrespective of their activity.
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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 | 0.001 | 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 | 0.001 |
| 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".