Information Diversity in Structure and Dynamics of Simulated Neuronal Networks

    Research output: Contribution to journalArticleScientificpeer-review

    8 Citations (Scopus)
    9 Downloads (Pure)

    Abstract

    Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.
    Original languageEnglish
    Article number26
    Pages (from-to)1-17
    Number of pages17
    JournalFrontiers in Computational Neuroscience
    Volume5
    Issue number26
    DOIs
    Publication statusPublished - 2011
    Publication typeA1 Journal article-refereed

    Publication forum classification

    • Publication forum level 1

    Fingerprint

    Dive into the research topics of 'Information Diversity in Structure and Dynamics of Simulated Neuronal Networks'. Together they form a unique fingerprint.

    Cite this