software version 6.0.7  (Bioedit Company)

Journal: bioRxiv

Article Title: Deep learning-based synaptic event detection

doi: 10.1101/2023.11.02.565316

Figure Lengend Snippet: a , Scheme of event detection benchmarking. Six methods are compared using precision and recall metrics. b , Event-free recordings were superimposed with generated events to create ground-truth data. Depicted example data have a signal-to-noise ratio (SNR) of 9 dB. c , Left: Output of the detection methods for data in b . Right: Improvement in SNR relative to the data. Note that MiniAnalysis is omitted from this analysis because the software does not provide output trace data. d , SNR from mEPSC recordings at MF–GC synapses (n = 170, whiskers cover full range of data). e–g , Recall, precision, and F1 score versus SNR for the six different methods. Data are averages of three independent runs for each SNR. h , Average F1 score versus event kinetics. Detection methods relying on an event template (template-matching, deconvolution and Bayesian) are not very robust to changes in event kinetics. i , Evaluating the threshold dependence of detection methods. Asterisk marks event close to detection threshold. j , Number of detected events vs. threshold (in % of default threshold value, range 5–195) for different methods. Dashed line indicates true event number.

Article Snippet: We compared the deep learning-based miniML with the following previously described detection methods: template-matching , deconvolution , a finite threshold-based approach , the commercial MiniAnalysis software (version 6.0.7, Synaptosoft), and a Bayesian inference procedure .

Techniques: Generated, Software