ArticleFrontiers in cellular neuroscience2025
A novel method for reliably measuring miniature and spontaneous postsynaptic events in whole-cell patch clamp recordings in the central nervous system.
Article in Frontiers in cellular neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Decoding gene networks controlling hypothalamic and prethalamic neuron development.Cell reports · 2025Article
- Decoding Gene Networks Controlling Hypothalamic and Prethalamic Neuron Development.bioRxiv : the preprint server for biology · 2025Article
- Average miniature post-synaptic potential size is inversely proportional to membrane capacitance across neocortical pyramidal neurons of different sizes.Frontiers in cellular neuroscience · 2025Article
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2 authors.
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Abstract
Measurements of miniature postsynaptic currents (mPSCs) or potentials (mPSPs) in the soma of neurons of the central nervous system (CNS) provide a way of quantifying the synaptic function at the network level and, therefore, are routine in the neurophysiology literature. These miniature responses (or minis) are thought to be elicited by the spontaneous release of a single neurotransmitter vesicle, also called a quantum. As such, their measurement at the soma can potentially offer a technically straightforward way of estimating "quantal sizes" of central synapses. However, popular methods for detecting minis in whole-cell recordings fall short of being able to reliably distinguish them from background physiological noise. This issue has received very limited attention in the literature, and its scope as well as the relative performance of existing algorithms have not been quantified. As a result, solutions for reliably measuring the quantal size in somatic recordings also do not exist. As the first step in proposing and testing a potential solution, we developed and described a novel miniature postsynaptic event detection algorithm as part of our quantal analysis software called "minis". We tested its performance in detecting real and simulated minis in whole-cell recordings from pyramidal neurons in rat neocortical slices and compared it to two of the most-used mini detection algorithms. This benchmarking revealed superior detection by our algorithm. The release version of the algorithm also offers great flexibility via graphical and programming interfaces.
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