Reproducible supervised learning-assisted classification of spontaneous synaptic waveforms with Eventer - Winchester, Giles; Steele, Oliver G.; Liu, Samuel; Maia Chagas, Andre; Aziz, Wajeeha; Penn, Andrew C. (Frontiers in Neuroinformatics, 2024)
Detection and analysis of spontaneous synaptic events is an extremely common task in many neuroscience research labs. Various algorithms and tools have been developed over the years to improve the sensitivity for detecting synaptic events. However, the final stages of most procedures for detecting synaptic events still involves manual selection of candidate events. This step in the analysis is laborious and requires care and attention to maintain consistency of event selection across the whole dataset. Manual selection can introduce bias and subjective selection criteria that cannot be shared with other labs simply in reporting methods.To address this, we have created Eventer, a standalone application for the detection of spontaneous synaptic events acquired by electrophysiology or imaging. This opensource application uses the freely available MATLAB Runtime and is deployed on Mac, Windows and Linux systems. The principle of the Eventer application is to learn the user's 'expert' strategy for classifying a set of detected event candidates from a small subset of the data, and then automatically apply the same criterion on the remaining dataset. Eventer first uses a suitable model template to pull out event candidates using Fast Fourier Transform (FFT)based deconvolution with a low threshold. Random forests are then created and trained to associate various features of the events with manual labelling. The stored model file can be reloaded and used to analyse large datasets with greater consistency. The availability of the source code and its user interface provide a framework with the scope to further tune the existing Random Forests implementation, or add additional, artificial intelligence classification methods.The eventer website () includes a repository where researchers can upload and share their machine learning model files and thereby provide greater opportunities for enhancing reproducibility when analysing datasets of spontaneous synaptic activity. In summary, Eventer, and the associated repository, could allow researchers studying synaptic transmission to increase throughput of their data analysis and address the increasing concerns of reproducibility in neuroscience research.