UC San Diego

The miniature excitatory post synaptic current (mEPSC) is a fundamental electrophysiological measurement of quantal vesicular release of transmitter at synapses. Typically, for excitatory transmission at central nervous system neurons, due to the small size of the electrical signal relative to the noise, recordings containing mEPSCs are analyzed by eye in conjunction with a basic peak/template finding algorithm. Such methods require expertise and considerable time, and generally require elimination of signals smaller than ~8-10 pA to avoid counting noise (i.e. false positives). Here we describe an entirely automated approach to detect mEPSCs using a machine learning based tool. This method eliminates inter observer bias, and permits accurate detection of spontaneous mEPSCs smaller than 10 pA from brain slice neurons. Using whole cell recordings from the soma of CA1 hippocampal slice neurons this tool can detect more than 95% of mEPSCs identified by a trained human observer. Importantly, this tool identifies few events as mEPSCs in a recording of the same cell when excitatory postsynaptic receptors are blocked, indicating a low false positive rate. Such recordings permit calculation of a true (96.6 ± 0.9%; n=10) and false (1.4 ± 0.1%; n=10) positive detection rate for mEPSCs. Using this new tool mEPSCs can be rapidly and accurately measured in an unbiased manner, and permits analysis of previously undetected mEPSCs.