# A novel method for reliably measuring miniature and spontaneous postsynaptic events in whole-cell patch clamp recordings in the central nervous system

## TL;DR

This paper introduces a new method to accurately detect miniature synaptic events in brain neuron recordings, improving the reliability of quantal size estimation.

## Contribution

The paper presents a novel and reliable algorithm for detecting miniature postsynaptic events in whole-cell recordings, outperforming existing methods.

## Key findings

- The new algorithm outperforms existing methods in detecting miniature postsynaptic events in whole-cell recordings.
- The algorithm's performance was validated using both real and simulated data from rat neocortical pyramidal neurons.
- The algorithm offers flexibility through graphical and programming interfaces for practical use.

## Abstract

Measurements of miniature postsynaptic currents (mPSCs) or potentials (mPSPs) in the soma of neurons of the central nervous system (CNS) provide a way of quantifying the synaptic function at the network level and, therefore, are routine in the neurophysiology literature. These miniature responses (or minis) are thought to be elicited by the spontaneous release of a single neurotransmitter vesicle, also called a quantum. As such, their measurement at the soma can potentially offer a technically straightforward way of estimating “quantal sizes” of central synapses. However, popular methods for detecting minis in whole-cell recordings fall short of being able to reliably distinguish them from background physiological noise. This issue has received very limited attention in the literature, and its scope as well as the relative performance of existing algorithms have not been quantified. As a result, solutions for reliably measuring the quantal size in somatic recordings also do not exist. As the first step in proposing and testing a potential solution, we developed and described a novel miniature postsynaptic event detection algorithm as part of our quantal analysis software called “minis”. We tested its performance in detecting real and simulated minis in whole-cell recordings from pyramidal neurons in rat neocortical slices and compared it to two of the most-used mini detection algorithms. This benchmarking revealed superior detection by our algorithm. The release version of the algorithm also offers great flexibility via graphical and programming interfaces.