ArticleProceedings of the National Academy of Sciences of the United States of America2024
Detecting unitary synaptic events with machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Article
- 'Mini analysis' misrepresents changes in synaptic properties due to incomplete event detection.The Journal of physiology · 2025Article
- Article
- Average miniature post-synaptic potential size is inversely proportional to membrane capacitance across neocortical pyramidal neurons of different sizes.Frontiers in cellular neuroscience · 2025Article
- Reproducible supervised learning-assisted classification of spontaneous synaptic waveforms with Eventer.Frontiers in neuroinformatics · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spontaneously occurring miniature excitatory postsynaptic currents (mEPSCs) are fundamental electrophysiological events produced by quantal vesicular transmitter release at synapses. Their analysis can provide important information regarding pre- and postsynaptic function. However, the small signal relative to recording noise requires expertise and considerable time for their identification. Furthermore, many mEPSCs smaller than ~8 pA are not well resolved (e.g., those produced at distant synapses or synapses with few receptor channels). Here, we describe an automated approach to detect mEPSCs using a machine learning-based tool. This method, which can be easily generalized to other one-dimensional signals, eliminates inter-observer bias, provides an estimate of its sensitivity and specificity and permits reliable detection of small (e.g., 5 pA) spontaneous unitary synaptic events.
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