Graph structure modeling for multi-neuronal spike data

International Meeting on High-Dimensional Data-Driven Science 2015

Akaho, Shotaro^1 ; Higuchi, Sho^2 ; Iwasaki, Taishi^3 ; Hino, Hideitsu^4 ; Tatsuno, Masami^5 ; Murata, Noboru^3

National Institute of Advanced Industrial Science and Technology, 1-1-1 Umezono, Ibaraki, Tsukuba

305-8568, Japan^1

NTT DATA Corporation, 3-9 Toyosu 3-chome, Koto-ku, Tokyo

135-8671, Japan^2

Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo

169-8555, Japan^3

University of Tsukuba, 1-1-1 Tennodai, Ibaraki, Tsukuba

305-8577, Japan^4

University of Lethbridge, Lethbridge

AB

T1K3M4, Canada^5

关键词: Estimation algorithm;    Graph structures;    Large datasets;    Matrix inversions;    Spike train;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/699/1/012012/pdf
DOI  :  10.1088/1742-6596/699/1/012012

来源: IOP

【 摘 要 】

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.