Bulletin of Mathematical Biology

, Volume 73, Issue 2, pp 325–343

Neural Population Modes Capture Biologically Realistic Large Scale Network Dynamics

Original Article

DOI: 10.1007/s11538-010-9573-9

Cite this article as:
Jirsa, V.K. & Stefanescu, R.A. Bull Math Biol (2011) 73: 325. doi:10.1007/s11538-010-9573-9

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. 94(1):018106, 2005; Stefanescu and Jirsa in PLoS Comput. Biol. 4(11):e1000219, 2008), which parsimoniously captures various types of population behavior. We numerically demonstrate that the reduced population mode system favorably captures the high-dimensional dynamics of neuron networks with an architecture involving homogeneous local connectivity and a large-scale, fiber-like connection with time delay.

Keywords

Network dynamics Neuron model Neural population Dimension reduction Time delay Heterogeneous coupling Parameter dispersion 

Copyright information

© Society for Mathematical Biology 2010

Authors and Affiliations

  1. 1.Theoretical Neuroscience GroupInstitute Sciences de Mouvement, UMR6233 CNRSMarseilleFrance
  2. 2.Center for Complex Systems and Brain Sciences, Department of PhysicsFlorida Atlantic UniversityBoca RatonUSA
  3. 3.Department of PhysicsFlorida Atlantic UniversityBoca RatonUSA

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