推断神经系统中定向功能连接的时序结构：格兰杰因果的一些扩展
应用统计
2019-07-17 v2
摘要
大脑中的神经过程在一系列时间尺度上运作。格兰杰因果（Granger causality）是从神经生理数据推断定向功能连接最广泛使用的神经科学工具，传统上不论数据采样率如何均以一步超前预测的形式部署，因此仅能对潜在神经过程的时序结构提供有限洞察。我们引入了基于多步、无限未来与单滞后预测的格兰杰因果变体，其有助于对大脑中信息流进行更细致与系统的时序分析。
引用
@article{arxiv.1904.03054,
title = {Inferring the temporal structure of directed functional connectivity in neural systems: some extensions to Granger causality},
author = {Lionel Barnett and Anil K. Seth},
journal= {arXiv preprint arXiv:1904.03054},
year = {2019}
}
备注
Accepted for presentation at the special session "Brain connectivity and neuronal system identification: theory and applications to brain state decoding", and for publication in conference proceedings, by the 9th Workshop on Brain-Machine Interface (BMI) at IEEE Systems, Man and Cybernetics (SMC) 2019, Bari, Italy, October 6-9, 2019