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Model-Based Inference of Synaptic Transmission
<span title="2019-08-20">2019</span>
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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5j7w5nihpbdjxp6k7que2k24au" style="color: black;">Frontiers in Synaptic Neuroscience</a>
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Synaptic computation is believed to underlie many forms of animal behavior. A correct identification of synaptic transmission properties is thus crucial for a better understanding of how the brain processes information, stores memories and learns. Recently, a number of new statistical methods for inferring synaptic transmission parameters have been introduced. Here we review and contrast these developments, with a focus on methods aimed at inferring both synaptic release statistics and synaptic
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnsyn.2019.00021">doi:10.3389/fnsyn.2019.00021</a>
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... dynamics. Furthermore, based on recent proposals we discuss how such methods can be applied to data across different levels of investigation: from intracellular paired experiments to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals.
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