Extracting nonlinear spatiotemporal dynamics in active dendrites using data-driven statistical approach

International Meeting on High-Dimensional Data-Driven Science 2015

Omori, Toshiaki^1 ; Hukushima, Koji^2,3

Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Hyogo, Kobe

657-8501, Japan^1

Department of Basic Science, Graduate School of Arts and Sciences, University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo

153-8902, Japan^2

Center for Materials Research by Information Integration, National Institute for Materials Science, 1-2-1 Sengen, Ibaraki, Tsukuba

305-0047, Japan^3

关键词: Data driven;    Membrane dynamics;    Probabilistic information;    Reaction diffusion equations;    Sequential Monte Carlo methods;    Spatio-temporal dynamics;    Statistical approach;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/699/1/012011/pdf
DOI  :  10.1088/1742-6596/699/1/012011

来源: IOP

【 摘 要 】

We propose a data-driven statistical method for extracting nonlinear spatiotemporal membrane dynamics of active dendrites. We employ a framework of probabilistic information processing to extract the nonlinear spatiotemporal dynamics obeying the reaction-diffusion equation from partially observable data. By employing sequential Monte-Carlo method and other statistical methods, membrane dynamics and their underlying electrical properties are simultaneously estimated in the proposed method. Using the proposed method, we show that nonlinear spatiotemporal dynamics in active dendrites can be extracted from partially observable data.