Pairwise Network Information and Nonlinear Correlations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00465842" target="_blank" >RIV/67985807:_____/16:00465842 - isvavai.cz</a>

Alternative codes found

RIV/00023752:_____/16:43915363

Result on the web

<a href="http://dx.doi.org/10.1103/PhysRevE.94.040301" target="_blank" >http://dx.doi.org/10.1103/PhysRevE.94.040301</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevE.94.040301" target="_blank" >10.1103/PhysRevE.94.040301</a>

Result language

angličtina

Original language name

Pairwise Network Information and Nonlinear Correlations

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BD - Information theory

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Physical Review E

ISSN

2470-0045

e-ISSN

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Volume of the periodical

94

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

6

Pages from-to

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UT code for WoS article

000388440600002

EID of the result in the Scopus database

2-s2.0-84994060943