Action potential bursts enhance transmitter release at a giant central synapse
Bibliographic record
Abstract
Patterns of action potentials (APs), often in the form of bursts, are critical for coding and processing information in the brain. However, how AP bursts modulate secretion at synapses remains elusive. Here, using the calyx of Held synapse as a model we compared synaptic release evoked by AP patterns with a different number of bursts while the total number of APs and frequency were fixed. The ratio of total release produced by multiple bursts to that by a single burst was defined as 'burst-effect'.We found that four bursts of 25 stimuli at 100 Hz increased the totalcharge of EPSCs to 1.47 ± 0.04 times that by a single burst of 100 stimuli at the same frequency.Blocking AMPA receptor desensitization and saturation did not alter the burst-effect, indicating that it was mainly determined by presynaptic mechanisms. Simultaneous dual recordings of presynaptic membrane capacitance (Cm) and EPSCs revealed a similar burst-effect, being 1.58±0.13by Cm and 1.49±0.05 by EPSCs. Reducing presynapticCa2+ influx by lowering extracellular Ca2+concentration or buffering residual intracellular Ca2+ with EGTA inhibited the burst-effect. We further developed a computational model largely recapitulating the burst-effect and demonstrated that this effect is highly sensitive to dynamic change in availability of the releasable pool of synaptic vesicles during various patterns of activities. Taken together, we conclude that AP bursts modulate synaptic output mainly through intricate interaction between depletion and replenishment of the large releasable pool. This burst-effect differs from the somatic burst-effect previously described from adrenal chromaffin cells, which substantially depends on activity-induced accumulation of Ca2+ to facilitate release of a limited number of vesicles in the releasable pool. Hence, AP bursts may play an important role in dynamically regulating synaptic strength and fidelity during intense neuronal activity at central 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) | 0.001 | 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".