Reproducible functional assays to study neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce , a MATLAB-based analysis tool for data-driven functional phenotyping of neuronal cultures recorded by high-density microelectrode arrays. is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply to human induced pluripotent stem cell–derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how enables phenotypic screenings.

中文翻译：

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DeePhys：用于人类神经元网络电生理表型分析的机器学习辅助平台

研究神经元网络的可重复功能测定是开发人类疾病生理相关细胞模型的重要基石。在这里，我们介绍一种基于 MATLAB 的分析工具，用于对高密度微电极阵列记录的神经元培养物进行数据驱动的功能表型分析。是一个模块化工作流程，提供了一系列从尖峰排序数据中提取特征的技术，允许在单个细胞和网络水平以及跨发育过程中检查功能表型。此外，还具有集成新颖功能和使用机器学习辅助方法的能力，这有助于对药理干预措施进行全面评估。为了说明其实际应用，我们将其应用于从患者和健康个体获得的人类诱导多能干细胞衍生的多巴胺能神经元，并展示如何进行表型筛选。