Pircher T, Pircher B, Feigenspan A (2022)
Publication Type: Journal article
Publication year: 2022
Book Volume: 17
Pages Range: e0273501-
Journal Issue: 9
DOI: 10.1371/journal.pone.0273501
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.
APA:
Pircher, T., Pircher, B., & Feigenspan, A. (2022). A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents. PLoS ONE, 17(9), e0273501-. https://dx.doi.org/10.1371/journal.pone.0273501
MLA:
Pircher, Thomas, Bianca Pircher, and Andreas Feigenspan. "A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents." PLoS ONE 17.9 (2022): e0273501-.
BibTeX: Download