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Macroscopic complexity from an autonomous network of networks of theta neurons.

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dc.contributor.author Luke, Tanushree B.
dc.contributor.author Barreto, Ernest
dc.contributor.author So, Paul
dc.date.accessioned 2015-09-23T15:22:56Z
dc.date.available 2015-09-23T15:22:56Z
dc.date.issued 2014-11-18
dc.identifier.citation Luke TB, Barreto E and So P (2014) Macroscopic complexity from an autonomous network of networks of theta neurons. Front. Comput. Neurosci. 8:145. doi: 10.3389/fncom.2014.00145 en_US
dc.identifier.uri https://hdl.handle.net/1920/9899
dc.description.abstract We examine the emergence of collective dynamical structures and complexity in a network of interacting populations of neuronal oscillators. Each population consists of a heterogeneous collection of globally-coupled theta neurons, which are a canonical representation of Type-1 neurons. For simplicity, the populations are arranged in a fully autonomous driver-response configuration, and we obtain a full description of the asymptotic macroscopic dynamics of this network. We find that the collective macroscopic behavior of the response population can exhibit equilibrium and limit cycle states, multistability, quasiperiodicity, and chaos, and we obtain detailed bifurcation diagrams that clarify the transitions between these macrostates. Furthermore, we show that despite the complexity that emerges, it is possible to understand the complicated dynamical structure of this system by building on the understanding of the collective behavior of a single population of theta neurons. This work is a first step in the construction of a mathematically-tractable network-of-networks representation of neuronal network dynamics.
dc.description.sponsorship Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund. en_US
dc.language.iso en_US en_US
dc.publisher Frontiers Media en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.subject theta neuron en_US
dc.subject type-I neuron en_US
dc.subject hierarchical network en_US
dc.subject neural field en_US
dc.subject macroscopic behavior en_US
dc.subject coherence en_US
dc.subject synchrony en_US
dc.subject chaos en_US
dc.title Macroscopic complexity from an autonomous network of networks of theta neurons. en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.3389/fncom.2014.00145


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