Authors: Viktor K Jirsa Roxana A Stefanescu
Publish Date: 2010/09/04
Volume: 73, Issue: 2, Pages: 325-343
Abstract
Large scale brain networks are understood nowadays to underlie the emergence of cognitive functions though the detailed mechanisms are hitherto unknown The challenges in the study of large scale brain networks are amongst others their high dimensionality requiring significant computational efforts the complex connectivity across brain areas and the associated transmission delays as well as the stochastic nature of neuronal processes To decrease the computational effort neurons are clustered into neural masses which then are approximated by reduced descriptions of population dynamics Here we implement a neural population mode approach Assisi et al in Phys Rev Lett 941018106 2005 Stefanescu and Jirsa in PLoS Comput Biol 411e1000219 2008 which parsimoniously captures various types of population behavior We numerically demonstrate that the reduced population mode system favorably captures the highdimensional dynamics of neuron networks with an architecture involving homogeneous local connectivity and a largescale fiberlike connection with time delay
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