Graph structure modeling for multi-neuronal spike date
Date
2016
Authors
Akaho, Shotaro
Higuchi, Sho
Iwasaki, Taishi
Hino, Hideitsu
Tatsuno, Masami
Murata, Noboru
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Publishing
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.
Description
Sherpa Romeo green journal; open access
Keywords
Spike data , Neurons , Pseudo-correlation
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