Neural complexity and structural connectivity
Neural complexity and structural connectivity
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DOI：
10.1103/physreve.79.051914
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发表时间：
2009-05-01
影响因子：
2.4
通讯作者：
Bullock, S.
中科院分区：
文献类型：
--
作者：
Barnett, L.;Buckley, C. L.;Bullock, S.
托诺尼[过程。国家。阿卡德。科学。 U.S.A. 91, 5033 (1994)]提出了一种基于给定神经网络的互补子系统之间的互信息的神经复杂性度量，这引起了神经科学界及其他领域的极大兴趣。我们开发了一种流行的高斯模型的近似度量，该模型应用于连续时间过程，阐明了神经系统的复杂性与其结构连通性之间的关系。此外，该近似对于弱耦合系统来说是准确的，并且计算成本低，随系统大小进行多项式缩放，这与呈指数缩放的完整复杂性度量相反。我们还讨论了连通性归一化并解决了由于原始高斯模型中的模糊性而产生的一些问题。
Tononi [Proc. Natl. Acad. Sci. U.S.A. 91, 5033 (1994)] proposed a measure of neural complexity based on mutual information between complementary subsystems of a given neural network, which has attracted much interest in the neuroscience community and beyond. We develop an approximation of the measure for a popular Gaussian model which, applied to a continuous-time process, elucidates the relationship between the complexity of a neural system and its structural connectivity. Moreover, the approximation is accurate for weakly coupled systems and computationally cheap, scaling polynomially with system size in contrast to the full complexity measure, which scales exponentially. We also discuss connectivity normalization and resolve some issues stemming from an ambiguity in the original Gaussian model.