Synapse specific and plasticity-regulated AMPAR mobility tunes synaptic integration
Bibliographic record
Abstract
Abstract Synaptic responses adapt to fast repetitive inputs during bursts of neuronal network activity over timescales of milliseconds to seconds, either transiently facilitating or depressing. This high-frequency stimulus-dependent short-term synaptic plasticity (HF-STP) relies on a number of molecular processes that collectively endow synapses with filtering properties for information processing, optimized for the transmission of certain input frequencies and patterns in distinct circuits 1–3 . Changes in HF-STP are traditionally thought to stem from changes in pre-synaptic transmitter release 1,2 , but post-synaptic modifications in receptor biophysical properties or surface diffusion also regulate HF-STP 4–11 . A major challenge in understanding synapse function is to decipher how pre- and post-synaptic mechanisms synergistically tune synaptic transmission efficacy during HF-STP, and to determine how neuronal activity modifies post-synaptic signal computation and integration to diversify neuronal circuit function. Here, taking advantage of new molecular tools to directly visualize glutamate release 12 and specifically manipulate the surface diffusion of endogenous AMPAR in intact circuits 13 , we define the respective contributions of pre-synaptic glutamate release, AMPAR desensitization and surface mobility to frequency-dependent synaptic adaptation. We demonstrate that post-synaptic gain control and signal integration capacity in synaptic networks is influenced by synapse-specific differences in AMPAR desensitization and diffusion-trapping characteristics that are shaped by molecular signaling events recruited during LTP.
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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) | 0.001 | 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".