A deep learning framework for automated and generalized synaptic event analysis.

ABSTRACT: Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring synaptic events carry fundamental information about synaptic function and plasticity. However, their stochastic nature and low signal-to-noise ratio present major challenges for the reliable and consistent analysis. Here, we introduce miniML, a supervised deep learning-based method for accurate classification and automated detection of spontaneous synaptic events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event analysis methods in terms of both precision and recall. miniML enables precise detection and quantification of synaptic events in electrophysiological recordings. We demonstrate that the deep learning approac

SUBMITTER: O'Neill PS 

PROVIDER: S-EPMC11882139 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature