Nonlinear Sciences – Adaptation and Self-Organizing Systems
Scientific paper
2004-11-04
Nonlinear Sciences
Adaptation and Self-Organizing Systems
6 pages, 4 figures
Scientific paper
10.1103/PhysRevE.71.016114
Considerable efforts in modern statistical physics is devoted to the study of networked systems. One of the most important example of them is the brain, which creates and continuously develops complex networks of correlated dynamics. An important quantity which captures fundamental aspects of brain network organization is the neural complexity C(X)introduced by Tononi et al. This work addresses the dependence of this measure on the topological features of a network in the case of gaussian stationary process. Both anlytical and numerical results show that the degree of complexity has a clear and simple meaning from a topological point of view. Moreover the analytical result offers a straightforward algorithm to compute the complexity than the standard one.
Bottaccio Maurizio
Lucia M. de
Montuori Marco
Pietronero Luciano
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