Detecting unitary synaptic events with machine learning

• 成果类型: Article
• 署名作者: Wang, Nien- Shao; Marino, Marc; Malinow, Roberto
• 署名单位: University of California System; University of California San Diego
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13520
• DOI: 10.1073/pnas.2315804121
• 发表日期: 2024-02-06
• 关键词: silent synapses

摘要

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 post-synaptic function. However, the small signal relative to recording noise requires expertise and considerable time for their identification. Furthermore, many mEPSCs smaller than similar to 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.