Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or meta...

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Main Authors: Douglas Zhou, Yanyang Xiao, Yaoyu Zhang, Zhiqin Xu, David Cai
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3929548?pdf=render
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spelling doaj-ed8248e2f931443d9a08e86ea3184eb42020-11-25T01:34:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0192e8763610.1371/journal.pone.0087636Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.Douglas ZhouYanyang XiaoYaoyu ZhangZhiqin XuDavid CaiReconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (I&F) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based I&F neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings.http://europepmc.org/articles/PMC3929548?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Douglas Zhou
Yanyang Xiao
Yaoyu Zhang
Zhiqin Xu
David Cai
spellingShingle Douglas Zhou
Yanyang Xiao
Yaoyu Zhang
Zhiqin Xu
David Cai
Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.
PLoS ONE
author_facet Douglas Zhou
Yanyang Xiao
Yaoyu Zhang
Zhiqin Xu
David Cai
author_sort Douglas Zhou
title Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.
title_short Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.
title_full Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.
title_fullStr Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.
title_full_unstemmed Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.
title_sort granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2014-01-01
description Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (I&F) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based I&F neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings.
url http://europepmc.org/articles/PMC3929548?pdf=render
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AT yaoyuzhang grangercausalitynetworkreconstructionofconductancebasedintegrateandfireneuronalsystems
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