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Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Univ Freiburg, Germany.
2016 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 6, e1004963Article in journal (Refereed) PublishedText
Abstract [en]

The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdos-Renyi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.

Place, publisher, year, edition, pages
2016. Vol. 12, no 6, e1004963
National Category
Bioinformatics (Computational Biology)
URN: urn:nbn:se:kth:diva-190513DOI: 10.1371/journal.pcbi.1004963ISI: 000379349700038PubMedID: 27271768ScopusID: 2-s2.0-84978791275OAI: diva2:953553

QC 2060818

Available from: 2016-08-18 Created: 2016-08-12 Last updated: 2016-08-18Bibliographically approved

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Jovanović, Stojan
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