Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package

scientific article published on 24 November 2020

Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1016/J.XPRO.2020.100176
P932PMC publication ID7757407
P698PubMed publication ID33377070

P2093author name stringMichael Lynn
Jean-Claude Béïque
Richard Naud
P2860cites workCannabinoid physiology and pharmacology: 30 years of progress.Q34355485
Membrane lipids tune synaptic transmission by direct modulation of presynaptic potassium channelsQ34401478
Endocannabinoid-mediated synaptic plasticity in the CNS.Q36507561
Statistical inference for noisy nonlinear ecological dynamic systemsQ43954821
Metaplasticity at CA1 Synapses by Homeostatic Control of Presynaptic Release DynamicsQ47991742
Characteristics of CA1 neurons recorded intracellularly in the hippocampalin vitro slice preparationQ48470636
Correlated Synaptic Inputs Drive Dendritic Calcium Amplification and Cooperative Plasticity during Clustered Synapse DevelopmentQ48923475
The NumPy Array: A Structure for Efficient Numerical ComputationQ57317251
A Synthetic Likelihood Solution to the Silent Synapse Estimation ProblemQ97650563
P4510describes a project that usesPythonQ28865
P433issue3
P921main subjectPythonQ28865
Python packageQ29642950
P6104maintained by WikiProjectWikiProject SoftwareQ15659621
P304page(s)100176
P577publication date2020-11-24
P1433published inSTAR ProtocolsQ96732630
P1476titleAccurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package
P478volume1

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