Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method.
Background: It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network functions.Results: In this study, the ideas of linear G...
| Publicado en: | BMC Neuroscience Vol. 21; no. 1; pp. 1 - 10 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2/12/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141726064&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141726064 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712202 1CI8 jtl: BMC Neuroscience issn: 14712202 maglogo: N pubinfo: dt: 2/12/2020 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 141726064 141726064 NLM32050908 141726064 10.1186/s12868-020-0555-z NLM32050908 141726064 ppf: 1 ppct: 9 formats: tig: atl: Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method. aug: au: Zhu, Mei-jia Dong, Chao-yi Chen, Xiao-yan Ren, Jing-wen Zhao, Xiao-yi affil: School of Electric Power, Inner Mongolia University of Technology, 010080, Hohhot, China sug: subj: Brain Mapping Methods Brain Physiology Models, Biological Neurons Physiology Algorithms Multivariate Analysis Chaos Theory Neural Pathways Physiology Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools ab: Background: It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network functions.Results: In this study, the ideas of linear Granger causality modeling and causality identification are extended to those of nonlinear Granger causality modeling and network structure identification. We employed Radial Basis Functions to fit the nonlinear multivariate dynamical responses of BNNs with neuronal pulse firing. By introducing the contributions from presynaptic neurons and detecting whether the predictions for postsynaptic neurons' pulse firing signals are improved or not, we can reveal the information flows distribution of BNNs. Thus, the functional connections from presynaptic neurons can be identified from the obtained network information flows. To verify the effectiveness of the proposed method, the Nonlinear Granger Causality Identification Method (NGCIM) is applied to the network structure discovery processes of Spiking Neural Networks (SNN). SNN is a simulation model based on an Integrate-and-Fire mechanism. By network simulations, the multi-channel neuronal pulse sequence data of the SNNs can be used to reversely identify the synaptic connections and strengths of the SNNs.Conclusions: The identification results show: for 2-6 nodes small-scale neural networks, 20 nodes medium-scale neural networks, and 100 nodes large-scale neural networks, the identification accuracy of NGCIM with the Gaussian kernel function was 100%, 99.64%, 98.64%, 98.37%, 98.31%, 84.87% and 80.56%, respectively. The identification accuracies were significantly higher than those of a traditional Linear Granger Causality Identification Method with the same network sizes. Thus, with an accumulation of the data obtained by the existing measurement methods, such as Electroencephalography, functional Magnetic Resonance Imaging, and Multi-Electrode Array, the NGCIM can be a promising network modeling method to infer the functional connective maps of BNNs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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