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

页码：38 / 46

页数：9

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