The gliotransmitter S100β regulates synaptic plasticity in the visual cortex
Bibliographic record
Abstract
SUMMARY Synaptic plasticity is a fundamental mechanism of memory storage in the brain. Among the various rules governing changes in synaptic strength, Spike Timing-Dependent Plasticity (STDP) stands out for its strong physiological relevance in vivo . Ubiquitous across brain regions and neuronal types, STDP is a complex and multifactorial process influenced by factors such as neuromodulation, extracellular calcium levels, and activity patterns. However, one relatively understudied factor is the role of astrocytes, despite their well-established involvement in regulating synaptic transmission and neuronal excitability through gliotransmitter release. While some factors have garnered significant attention, others, like S100β, have remained relatively underexplored despite their potential importance in regulating synaptic plasticity. S100β is a calcium-binding protein, allowing it to influence extracellular Ca²⁺ concentration and potentially all Ca 2+ -dependent plasticity processes. Building on our previous research in the visual cortex, where we examined the regulation of neuronal excitability by S100β, we chose to further investigate the role of astrocytes and S100β in synaptic plasticity at layer 2/3-layer 5 synapses in the visual cortex. We demonstrated that S100β is an important gliotransmitter to consider, capable of regulating long-term potentiation.
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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".