Granger causality-based synaptic weights estimation for analyzing neuronal networks

Pei Chiang Shao, Jian Jia Huang, Wei Chang Shann, Chen Tung Yen, Meng Li Tsai, Chien Chang Yen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC measure is designed to be nonnegative in its original form, lacking of the trait for differentiating the effects of excitations and inhibitions between neurons. (2) How is the estimated causality related to the underlying synaptic weights? Based on the GC, we propose a computational algorithm under a best linear predictor assumption for analyzing neuronal networks by estimating the synaptic weights among them. Under this assumption, the GC analysis can be extended to measure both excitatory and inhibitory effects between neurons. The method was examined by three sorts of simulated networks: those with linear, almost linear, and nonlinear network structures. The method was also illustrated to analyze real spike train data from the anterior cingulate cortex (ACC) and the striatum (STR). The results showed, under the quinpirole administration, the significant existence of excitatory effects inside the ACC, excitatory effects from the ACC to the STR, and inhibitory effects inside the STR.

Original languageEnglish
Pages (from-to)483-497
Number of pages15
JournalJournal of Computational Neuroscience
Volume38
Issue number3
DOIs
StatePublished - 1 Jun 2015

Keywords

  • Granger causality analysis
  • Neuronal networks
  • Synaptic weights estimation
  • Vector autoregressive model

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