Glial Cells Decipher Synaptic Competition at the Mammalian Neuromuscular Junction
Bibliographic record
Abstract
It is now accepted that glial cells actively interact with neurons and modulate their activity in many regions of the nervous system. Importantly, modulation of synaptic activity by glial cells depends on the proper detection and decoding of synaptic activity. However, it remains unknown whether glial cells are capable of decoding synaptic activity and properties during early postdevelopmental stages, in particular when different presynaptic nerve terminals compete for the control of the same synaptic site. This may be particularly relevant because a major determinant of the outcome of synaptic competition process is the relative synaptic strength of competing terminals whereby stronger terminals are more likely to occupy postsynaptic territory and become stabilized while weaker terminals are often eliminated. Hence, because of their ability to decode synaptic activity, glial cells should be able to integrate neuronal information of competing terminals. Using simultaneous glial Ca(2+) imaging and synaptic recordings of dually innervated mouse neuromuscular junctions, we report that single glial cells decipher the strength of competing nerve terminals. Activity of single glial cells, revealed by Ca(2+) responses, reflects the synaptic strength of each competing nerve terminal and the state of synaptic competition. This deciphering is mediated by functionally segregated purinergic receptors and intrinsic properties of glial cells. Our results indicate that glial cells decode ongoing synaptic competition and, hence, are poised to influence its outcome.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".