4.4 Article

Neural Population Modes Capture Biologically Realistic Large Scale Network Dynamics

期刊

BULLETIN OF MATHEMATICAL BIOLOGY
卷 73, 期 2, 页码 325-343

出版社

SPRINGER
DOI: 10.1007/s11538-010-9573-9

关键词

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

资金

  1. JS McDonnell Foundation
  2. ATIP (CNRS)

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据