Estimating time-varying directed neural networks

作     者：Wang, Haixu Cao, Jiguo 

作者机构：Simon Fraser Univ Dept Stat & Actuarial Sci Burnaby BC V5A 1S6 Canada 

出 版 物：《STATISTICS AND COMPUTING》 (统计学与计算)

年 卷 期：2020年第30卷第5期

页      面：1209-1220页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2018-06008] 

主　　题：Differential equation Neural data analysis Network reconstruction Poisson process Spike sequence 

摘      要：Reconstructing the functional network of a neuron cluster is a fundamental step to reveal the complex interactions among neural systems of the brain. Current approaches to reconstruct a network of neurons or neural systems focus on establishing a static network by assuming the neural network structure does not change over time. To the best of our knowledge, this is the first attempt to build a time-varying directed network of neurons by using an ordinary differential equation model, which allows us to describe the underlying dynamical mechanism of network connections. The proposed method is demonstrated by estimating a network of wide dynamic range neurons located in the dorsal horn of the rats spinal cord in response to pain stimuli applied to the Zusanli acupoint on the right leg. The finite sample performance of the proposed method is also investigated with a simulation study.