Graph structure modeling for multi-neuronal spike data

Shotaro Akaho, Sho Higuchi, Taishi Iwasaki, Hideitsu Hino, Masami Tatsuno, Noboru Murata

研究成果: Conference article査読

抄録

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.

本文言語English
論文番号012012
ジャーナルJournal of Physics: Conference Series
699
1
DOI
出版ステータスPublished - 2016 4 6
イベントInternational Meeting on High-Dimensional Data-Driven Science, HD3 2015 - Kyoto, Japan
継続期間: 2015 12 142015 12 17

ASJC Scopus subject areas

  • Physics and Astronomy(all)

フィンガープリント 「Graph structure modeling for multi-neuronal spike data」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル