Building population models for large-scale neural recordings: Opportunities and pitfalls

被引：19

作者：

Hurwitz, Cole ^{[1
]}

Kudryashova, Nina ^{[1
]}

Onken, Arno ^{[1
]}

Hennig, Matthias H. ^{[1
]}

机构：

[1] Univ Edinburgh, Inst Adapt & Neural Computat Edinburgh, Edinburgh EH8 9AB, Midlothian, Scotland

来源：

CURRENT OPINION IN NEUROBIOLOGY | 2021年 / 70卷

基金：

英国工程与自然科学研究理事会;

关键词：

GAUSSIAN-PROCESS; SPIKE TRAINS; DYNAMICS; FRAMEWORK;

D O I：

10.1016/j.conb.2021.07.003

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here, we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.

引用

页码：64 / 73

页数：10

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