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

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 precl...

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Bibliographic Details
Published in:PloS one Vol. 17; no. 9; p. e0273501
Main Authors: Pircher, Thomas, Pircher, Bianca, Feigenspan, Andreas
Format: Journal Article
Language:English
Published: San Francisco Public Library of Science 19.09.2022
Public Library of Science (PLoS)
Subjects:
Algorithms
Automation
Bias
Biology and Life Sciences
Cell culture
Central nervous system
Computer and Information Sciences
Cortex (auditory)
Decision making
Engineering and Technology
Excitatory postsynaptic potentials
Human bias
Hypotheses
Learning algorithms
Machine learning
Neural circuitry
Neural networks
Neural transmission
Neurological research
Neurotransmitter release
Neurotransmitters
Physical Sciences
Research and Analysis Methods
Retina
Signal processing
Time series
Germany
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Description
Summary: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.
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Competing Interests: The authors have declared that no completing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0273501