Graph structure modeling for multi-neuronal spike date

dc.contributor.author	Akaho, Shotaro
dc.contributor.author	Higuchi, Sho
dc.contributor.author	Iwasaki, Taishi
dc.contributor.author	Hino, Hideitsu
dc.contributor.author	Tatsuno, Masami
dc.contributor.author	Murata, Noboru
dc.date.accessioned	2017-03-17T18:40:38Z
dc.date.available	2017-03-17T18:40:38Z
dc.date.issued	2016
dc.description	Sherpa Romeo green journal; open access	en_US
dc.description.abstract	We propose a method to extract connectivity between neurons for extracellularly recorded multiple spike trains. The method removes pseudo-correlation caused by propagation of information along an indirect pathway, and is also robust against the in uence from unobserved neurons. The estimation algorithm consists of iterations of a simple matrix inversion, which is scalable to large data sets. The performance is examined by synthetic spike data.	en_US
dc.identifier.citation	Akaho, S., Higuchi, S., Iwasaki, T., Hino, H., Tatsuno, M., & Murata, N. (2016). Graph structure modeling for multi-neuronal spike data. Journal of Physics: Conference Series, 699. doi:10.1088/1742-6596/699/1/012012	en_US
dc.identifier.uri	https://hdl.handle.net/10133/4799
dc.language.iso	en_US	en_US
dc.publisher	IOP Publishing	en_US
dc.subject	Spike data	en_US
dc.subject	Neurons	en_US
dc.subject	Pseudo-correlation	en_US
dc.title	Graph structure modeling for multi-neuronal spike date	en_US
dc.type	Article	en_US