TY - JOUR
T1 - Graph structure modeling for multi-neuronal spike data
AU - Akaho, Shotaro
AU - Higuchi, Sho
AU - Iwasaki, Taishi
AU - Hino, Hideitsu
AU - Tatsuno, Masami
AU - Murata, Noboru
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2016/4/6
Y1 - 2016/4/6
N2 - 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 influence 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.
AB - 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 influence 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.
UR - http://www.scopus.com/inward/record.url?scp=84964872220&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964872220&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/699/1/012012
DO - 10.1088/1742-6596/699/1/012012
M3 - Conference article
AN - SCOPUS:84964872220
SN - 1742-6588
VL - 699
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012012
T2 - International Meeting on High-Dimensional Data-Driven Science, HD3 2015
Y2 - 14 December 2015 through 17 December 2015
ER -