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Efficient Network Reconstruction from Dynamical Cascades Identifies Small-World Topology of Neuronal Avalanches
Authors:Sinisa Pajevic and Dietmar Plenz
Affiliation:1.Mathematical and Statistical Computing Laboratory, Division ofComputational Bioscience, Center for Information Technology, National Institutesof Health, Bethesda, Maryland, United States of America;2.Section on Critical Brain Dynamics, Laboratory of Systems Neuroscience,National Institute of Mental Health, National Institutes of Health, Bethesda,Maryland, United States of America;Indiana University, United States of America
Abstract:Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB) and Posterior Weighted Averaging (PWA) methods. We introduce a special case of PWA, cast in nonparametric form, which we call the normalized count (NC) algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from subcritical, critical, and supercritical cascading dynamics and yields significant improvements over commonly used correlation methods. With experimental data, NC identified a functional and structural small-world topology and its corresponding traffic in cortical networks with neuronal avalanche dynamics.
Keywords:
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