Method for analyzing time-varying statistics on point process data with multiple trials

被引：1
|
作者：
Fujiwara, Kantaro [1 ]
Suzuki, Hideyuki [2 ,3 ]
Ikeguchi, Tohru [1 ]
Aihara, Kazuyuki [2 ,3 ]
机构：
[1] Tokyo Univ Sci, Dept Management Sci, Shinjuku Ku, 1-3 Kagurazaka, Tokyo 1628601, Japan
[2] Univ Tokyo, Grad Sch Informat Sci & Technol, Bunkyo Ku, Tokyo 1138656, Japan
[3] Univ Tokyo, Inst Ind Sci, Meguro Ku, Tokyo 1538505, Japan
来源：
IEICE NONLINEAR THEORY AND ITS APPLICATIONS | 2015年 / 6卷 / 01期
关键词：
time-series analysis; statistics; time-variability; neuroscience; neural coding;
D O I：
10.1587/nolta.6.38
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
Many stochastic systems require multiple trials to estimate their time-varying statistics. Time-varying statistics are often estimated by employing a time window of a certain length over trials. However, no standardized method exists for estimating time-varying statistics. In this paper, we propose an analysis method for measuring time-varying statistics that can be applied to point process data with multiple trials, such as neural spike trains.
引用
收藏
页码：38 / 46
页数：9
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