O'Neill Philipp S, Baccino-Calace Martín, Rupprecht Peter, Lee Sungmoo, Hao Yukun A, Lin Michael Z, Friedrich Rainer W, Mueller Martin, Delvendahl Igor
Department of Molecular Life Sciences, University of Zurich (UZH), Zurich, Switzerland.
Neuroscience Center Zurich, Zurich, Switzerland.
Elife. 2025 Mar 5;13:RP98485. doi: 10.7554/eLife.98485.
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.
关于突触传递的定量信息是我们理解神经功能的关键。自发发生的突触事件携带有关突触功能和可塑性的基本信息。然而，它们的随机性质和低信噪比给可靠且一致的分析带来了重大挑战。在此，我们介绍了miniML，这是一种基于监督深度学习的方法，用于对自发突触事件进行准确分类和自动检测。使用模拟的真实数据进行的比较分析表明，miniML在精度和召回率方面均优于现有的事件分析方法。miniML能够在电生理记录中精确检测和量化突触事件。我们证明，深度学习方法能够轻松推广到不同的突触标本、不同的电生理和光学记录技术以及不同的动物物种。miniML不仅为突触事件的自动化、可靠且标准化分析提供了一个全面而强大的框架，还为神经功能和功能障碍的高通量研究开辟了新途径。
