Multiple neural spike train data analysis: state-of-the-art and future challenges

被引：580

作者：

Brown, EN

Kass, RE

Mitra, PP

机构：

[1] Harvard Univ, Sch Med, MIT, Div Hlth Sci & Technol, Boston, MA 02114 USA

[2] Carnegie Mellon Univ, Dept Stat, Pittsburgh, PA 15208 USA

[3] Ctr Neural Basis Cognit, Dept Stat, Pittsburgh, PA 15208 USA

[4] Cold Spring Harbor Lab, Cold Spring Harbor, NY 11724 USA

来源：

NATURE NEUROSCIENCE | 2004年 / 7卷 / 05期

关键词：

D O I：

10.1038/nn1228

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Multiple electrodes are now a standard tool in neuroscience research that make it possible to study the simultaneous activity of several neurons in a given brain region or across different regions. The data from multi-electrode studies present important analysis challenges that must be resolved for optimal use of these neurophysiological measurements to answer questions about how the brain works. Here we review statistical methods for the analysis of multiple neural spike-train data and discuss future challenges for methodology research.

引用

页码：456 / 461

页数：6

共 65 条

[1] Parameter estimation in a model for multidimensional recording of neuronal data:: a Gibbsian approximation approach [J].