A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents

Thomas Pircher, Bianca Pircher and Andreas Feigenspan

PLOS ONE, 2022, vol. 17, issue 9, 1-16

Abstract: Spontaneous synaptic activity is a hallmark of biological neural networks. A thorough description of these synaptic signals is essential for understanding neurotransmitter release and the generation of a postsynaptic response. However, the complexity of synaptic current trajectories has either precluded an in-depth analysis or it has forced human observers to resort to manual or semi-automated approaches based on subjective amplitude and area threshold settings. Both procedures are time-consuming, error-prone and likely affected by human bias. Here, we present three complimentary methods for a fully automated analysis of spontaneous excitatory postsynaptic currents measured in major cell types of the mouse retina and in a primary culture of mouse auditory cortex. Two approaches rely on classical threshold methods, while the third represents a novel machine learning-based algorithm. Comparison with frequently used existing methods demonstrates the suitability of our algorithms for an unbiased and efficient analysis of synaptic signals in the central nervous system.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0273501

DOI: 10.1371/journal.pone.0273501

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