Graph structure modeling for multi-neuronal spike data. (March 2016)

• Record Type: Journal Article
• Authors: Akaho, Shotaro; Higuchi, Sho; Iwasaki, Taishi; Hino, Hideitsu; Tatsuno, Masami; Murata, Noboru
• Is Part Of: Journal of physics. Volume 699(2016)
• Publication Date: 2016-03
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/699/1/012012
• Languages: English
• ISSNs: 1742-6588

Abstract

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 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.