Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

Characterization of an electrically active cell, such as a neuron, demands measurement of its electrical properties. Due to differences in gene activation, location, innervation patterns, and functions, the millions of neurons in the mammalian brain are tremendously diverse in their membrane characteristics and abilities to generate action potentials. These features can be measured with a patch-clamp technique in whole-cell current-clamp configuration followed by detailed post-hoc analysis of firing patterns. This analysis can be time-consuming, and different laboratories have their own methods to perform it, either manually or with custom-written scripts. Here, we describe in detail a protocol for firing-pattern registration in neurons of the ventral tegmental area (VTA) as an example and introduce a software for its fast and convenient analysis. With the help of this article, other research groups can easily apply this method and generate unified types of data that are comparable between brain regions and various studies. Graphic abstract: Workflow of the Protocol.