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Detecting unitary synaptic events with machine learning.
Published Web Location
https://doi.org/10.1073/pnas.2315804121Abstract
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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