A general method to generate artificial spike train populations matching recorded neurons

DOI10.1007/s10827-020-00741-wzbMath1431.92017OpenAlexW2999790205WikidataQ92892149 ScholiaQ92892149MaRDI QIDQ2300399

Dieter Jaeger, Selva Maran, Samira Abbasi

Publication date: 27 February 2020

Published in: Journal of Computational Neuroscience (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036336

zbMATH Keywords

correlationmodelcerebellumcortex\textit{in vivo}synaptic

Mathematics Subject Classification ID

Neural biology (92C20)

Cites Work

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• Estimating Spiking Irregularities Under Changing Environments
• Generation of Correlated Spike Trains
• Generating Spike Trains with Specified Correlation Coefficients
• Differences in Spiking Patterns Among Cortical Neurons
• Generation of Spike Trains with Controlled Auto- and Cross-Correlation Functions
• Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events
• Generation of Synthetic Spike Trains with Defined Pairwise Correlations
• Polychronization: Computation with Spikes

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