Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data

Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data

The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as...

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Podrobná bibliografie
Hlavní autoři: Ghosh, S., Chen, Zhe, Putrino, David F., Barbieri, Riccardo, Brown, Emery N., Tseng, Mitchell, Sharif, Naubaha
Další autoři: Harvard University--MIT Division of Health Sciences and Technology
Médium: Článek
Jazyk:en_US
Vydáno: Institute of Electrical and Electronics Engineers (IEEE) 2012
On-line přístup:http://hdl.handle.net/1721.1/70044
https://orcid.org/0000-0003-2668-7819
https://orcid.org/0000-0002-6166-448X

Internet

http://hdl.handle.net/1721.1/70044
https://orcid.org/0000-0003-2668-7819
https://orcid.org/0000-0002-6166-448X

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