Detecting the topology of a neural network from partially obtained data using piecewise granger causality

Xiaoqun Wu; Changsong ZHOU; Jun Wang; Jun An Lu

doi:10.1007/978-3-642-21105-8_21

Detecting the topology of a neural network from partially obtained data using piecewise granger causality

Xiaoqun Wu^*, Changsong ZHOU, Jun Wang, Jun An Lu

^*Corresponding author for this work

Department of Physics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

3 Citations (Scopus)

Abstract

The dynamics and function of a network are influenced by the topology of the network. A great need exists for the development of effective methods of inferring network structure. In the past few years, topology identification of complex networks has received intensive interest and quite a few works have been published in literature. However, in most of the publications, each state of a multidimensional node in the network has to be observable, and usually the nodal dynamics is assumed known. In this paper, a new method of recovering the underlying directed connections of a network from the observation of only one state of each node is proposed. The validity of the proposed approach is illustrated with a coupled FitzHugh-Nagumo neurobiological network by only observing the membrane potential of each neuron and found to outperform the traditional Granger causality method. The network coupling strength and noise intensity which might also affect the effectiveness of our method are further analyzed.

Original language	English
Title of host publication	Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011
Pages	166-175
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-21105-8_21
Publication status	Published - 2011
Event	8th International Symposium on Neural Networks, ISNN 2011 - Guilin, China Duration: 29 May 2011 → 1 Jun 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6675 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Symposium on Neural Networks, ISNN 2011
Country/Territory	China
City	Guilin
Period	29/05/11 → 1/06/11

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

User-Defined Keywords

Complex networks
Granger causality
stochastic process
topology identification

Access to Document

10.1007/978-3-642-21105-8_21

Cite this

Wu, X., ZHOU, C., Wang, J., & Lu, J. A. (2011). Detecting the topology of a neural network from partially obtained data using piecewise granger causality. In Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011 (PART 1 ed., pp. 166-175). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6675 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-21105-8_21

Wu, Xiaoqun ; ZHOU, Changsong ; Wang, Jun et al. / Detecting the topology of a neural network from partially obtained data using piecewise granger causality. Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 1. ed. 2011. pp. 166-175 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Detecting the topology of a neural network from partially obtained data using piecewise granger causality",

abstract = "The dynamics and function of a network are influenced by the topology of the network. A great need exists for the development of effective methods of inferring network structure. In the past few years, topology identification of complex networks has received intensive interest and quite a few works have been published in literature. However, in most of the publications, each state of a multidimensional node in the network has to be observable, and usually the nodal dynamics is assumed known. In this paper, a new method of recovering the underlying directed connections of a network from the observation of only one state of each node is proposed. The validity of the proposed approach is illustrated with a coupled FitzHugh-Nagumo neurobiological network by only observing the membrane potential of each neuron and found to outperform the traditional Granger causality method. The network coupling strength and noise intensity which might also affect the effectiveness of our method are further analyzed.",

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Wu, X, ZHOU, C, Wang, J & Lu, JA 2011, Detecting the topology of a neural network from partially obtained data using piecewise granger causality. in Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6675 LNCS, pp. 166-175, 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, 29/05/11. https://doi.org/10.1007/978-3-642-21105-8_21

Detecting the topology of a neural network from partially obtained data using piecewise granger causality. / Wu, Xiaoqun; ZHOU, Changsong; Wang, Jun et al.
Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 1. ed. 2011. p. 166-175 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6675 LNCS, No. PART 1).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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N2 - The dynamics and function of a network are influenced by the topology of the network. A great need exists for the development of effective methods of inferring network structure. In the past few years, topology identification of complex networks has received intensive interest and quite a few works have been published in literature. However, in most of the publications, each state of a multidimensional node in the network has to be observable, and usually the nodal dynamics is assumed known. In this paper, a new method of recovering the underlying directed connections of a network from the observation of only one state of each node is proposed. The validity of the proposed approach is illustrated with a coupled FitzHugh-Nagumo neurobiological network by only observing the membrane potential of each neuron and found to outperform the traditional Granger causality method. The network coupling strength and noise intensity which might also affect the effectiveness of our method are further analyzed.

AB - The dynamics and function of a network are influenced by the topology of the network. A great need exists for the development of effective methods of inferring network structure. In the past few years, topology identification of complex networks has received intensive interest and quite a few works have been published in literature. However, in most of the publications, each state of a multidimensional node in the network has to be observable, and usually the nodal dynamics is assumed known. In this paper, a new method of recovering the underlying directed connections of a network from the observation of only one state of each node is proposed. The validity of the proposed approach is illustrated with a coupled FitzHugh-Nagumo neurobiological network by only observing the membrane potential of each neuron and found to outperform the traditional Granger causality method. The network coupling strength and noise intensity which might also affect the effectiveness of our method are further analyzed.

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Wu X, ZHOU C, Wang J, Lu JA. Detecting the topology of a neural network from partially obtained data using piecewise granger causality. In Advances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011. PART 1 ed. 2011. p. 166-175. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-21105-8_21

Detecting the topology of a neural network from partially obtained data using piecewise granger causality

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