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Reproducible supervised learning-assisted classification of spontaneous synaptic waveforms with Eventer.

Giles Winchester1, Oliver G Steele1, Samuel Liu1

  • 1School of Life Sciences, University of Sussex, Brighton, United Kingdom.

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|September 30, 2024
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Eventer is a new open-source application that automates the detection of spontaneous synaptic events. It learns user criteria to improve consistency and reproducibility in neuroscience data analysis.

Keywords:
analysisevent detectionmachine learningreproducibilitysynapses

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Data Analysis

Background:

  • Manual detection of spontaneous synaptic events is time-consuming and prone to bias.
  • Existing methods for synaptic event detection lack consistency and reproducibility.
  • Automated tools are needed to streamline the analysis of electrophysiology and imaging data.

Purpose of the Study:

  • To develop an open-source application, Eventer, for automated detection and analysis of spontaneous synaptic events.
  • To provide a reproducible and consistent method for analyzing large datasets.
  • To reduce subjectivity and improve the throughput of synaptic event analysis.

Main Methods:

  • Eventer utilizes a machine learning approach, specifically Random Forests, trained on user-defined criteria.
  • The application employs Fast Fourier Transform (FFT)-based deconvolution for initial event candidate identification.
  • It is a standalone application compatible with Mac, Windows, and Linux, using the MATLAB Runtime.

Main Results:

  • Eventer successfully learns user-defined criteria for classifying synaptic events.
  • The application demonstrates high consistency in analyzing large datasets compared to manual selection.
  • An associated online repository facilitates sharing of machine learning models to enhance reproducibility.

Conclusions:

  • Eventer offers a reproducible and efficient solution for analyzing spontaneous synaptic events.
  • The open-source nature and model-sharing repository promote collaboration and standardization in neuroscience research.
  • This tool addresses critical needs for increased throughput and reproducibility in synaptic transmission studies.