Neural complexity and structural connectivity

作     者：L. Barnett C. L. Buckley S. Bullock 

作者机构：[]Department of Informatics Centre for Computational Neuroscience and Robotics School of Science and Technology University of Sussex Brighton BN1 9QH United Kingdom 

出 版 物：《Physical Review E》 (物理学评论E辑：统计、非线性和软体物理学)

年 卷 期：2009年第79卷第5期

页      面：051914-051914页

核心收录：

学科分类：07[理学] 070203[理学-原子与分子物理] 0702[理学-物理学] 

基　　金：EPSRC-GB (U.K.) [EP/C51632X/1] 

主　　题：.Gaussian PHYSICAL REVIEW approximation Normalization neural network Proc Natl subsystems cheap continuous-time neural systems system size Neural complexity structural connectivity complexity measure 

摘      要：Tononi et al. [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.