Reconstructing the structural connectivity between interacting units from observed activity is a challenge across many different disciplines. The fundamental first step is to establish whether or to what extent the interactions between the units can be considered pairwise and, thus, can be modeled as an interaction network with simple links corresponding to pairwise interactions. In principle, this can be determined by comparing the maximum entropy given the bivariate probability distributions to the true joint entropy. In many practical cases, this is not an option since the bivariate distributions needed may not be reliably estimated or the optimization is too computationally expensive. Here we present an approach that allows one to use mutual informations as a proxy for the bivariate probability distributions. This has the advantage of being less computationally expensive and easier to estimate. We achieve this by introducing a novel entropy maximization scheme that is based on conditioning on entropies and mutual informations. This renders our approach typically superior to other methods based on linear approximations. The advantages of the proposed method are documented using oscillator networks and a resting-state human brain network as generic relevant examples.

Complexity Science Group Department of Physics and Astronomy University of Calgary Calgary Alberta Canada T2N 1N4

Institute of Computer Science The Czech Academy of Sciences Pod vodarenskou vezi 2 18207 Prague Czech Republic

National Institute of Mental Health Topolová 748 250 67 Klecany Czech Republic

Citace poskytuje Crossref.org

Network Inference and Maximum Entropy Estimation on Information Diagrams