Detecting unitary synaptic events with machine learning

被引:2
|
作者
Wang, Nien- Shao [1 ]
Marino, Marc [1 ]
Malinow, Roberto [1 ]
机构
[1] Univ Calif San Diego, Dept Neurosci, Ctr Neural Circuits & Behav, Div Biol, La Jolla, CA 92093 USA
关键词
synapse; miniature; machine learning; SILENT SYNAPSES;
D O I
10.1073/pnas.2315804121
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
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.
引用
收藏
页数:5
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