ArticleeLife2025
A deep learning framework for automated and generalized synaptic event analysis.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Spike inference from calcium imaging data acquired with GCaMP8 indicators.Nature methods · 2026Article
- Article
- Automated ROI detection allows rapid quantification of synaptic activity across tens of thousands of synapses in cell culture.Frontiers in synaptic neuroscience · 2026Article
- Widely Used CaMKII Regulatory Segment Mutations Cause Tight Actinin Binding and Dendritic Spine Enlargement in Unstimulated Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- MicroRNA-138-5p suppresses excitatory synaptic strength at the cerebellar input layer.The Journal of physiology · 2025Article
- Mitochondrial dysfunction drives a neuronal exhaustion phenotype in methylmalonic aciduria.Communications biology · 2025Article
- Article
- In vivo microelectrode arrays for neuroscience.Nature reviews. Methods primers · 2025Article
- A fast and responsive voltage indicator with enhanced sensitivity for unitary synaptic events.Neuron · 2024Article
- Reproducible supervised learning-assisted classification of spontaneous synaptic waveforms with Eventer.Frontiers in neuroinformatics · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring synaptic events carry fundamental information about synaptic function and plasticity. However, their stochastic nature and low signal-to-noise ratio present major challenges for the reliable and consistent analysis. Here, we introduce miniML, a supervised deep learning-based method for accurate classification and automated detection of spontaneous synaptic events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event analysis methods in terms of both precision and recall. miniML enables precise detection and quantification of synaptic events in electrophysiological recordings. We demonstrate that the deep learning approach generalizes easily to diverse synaptic preparations, different electrophysiological and optical recording techniques, and across animal species. miniML provides not only a comprehensive and robust framework for automated, reliable, and standardized analysis of synaptic events, but also opens new avenues for high-throughput investigations of neural function and dysfunction.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.