Instruct: Identify the main category of Biorxiv papers based on the titles and abstracts
Query: A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents Spontaneous synaptic activity is a hallmark of 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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The categories of this paper are: genomics
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The categories of this paper are: and
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The categories of this paper are: behavior
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The categories of this paper are: synthetic
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The categories of this paper are: toxicology
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The categories of this paper are: genetics
31
The categories of this paper are: paleontology
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The categories of this paper are: microbiology
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The categories of this paper are: ecology
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The categories of this paper are: education
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The categories of this paper are: plant
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The categories of this paper are: bioengineering
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The categories of this paper are: cancer
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The categories of this paper are: cognition
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The categories of this paper are: biophysics
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The categories of this paper are: molecular