Iterative procedure for network inference

Acknowledgements This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642563. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Peer reviewedPostprin

Analytical approach to network inference : Investigating degree distribution

The authors thank Dr. Daniel Vogel for helpful comments and discussions. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642563.Peer reviewedPublisher PD

The effect of latent confounding processes on the estimation of the strength of causal influences in chain-type networks

The authors acknowledge GTD TauRx Therapeutics centres for generous funding of this research.Peer reviewedPublisher PD

Inferring the underlying multivariate structure from bivariate networks with highly correlated nodes

Funding Information: PL acknowledges financial support from Medical Research Scotland (Grant No.: RG14565). Publisher Copyright: © 2022, The Author(s).Peer reviewedPublisher PD

Networks : On the relation of bi- and multivariate measures

Date of Acceptance: 28/04/2015 Acknowledgement The article processing charge was funded by the German Research Foundation (DFG) and the Albert Ludwigs University Freiburg in the funding programme Open Access PublishingPeer reviewedPublisher PD

A numerically efficient implementation of the expectation maximization algorithm for state space models

Peer reviewedPostprin

Improving network inference : The impact of false positive and false negative conclusions about the presence or absence of links

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 642563.Peer reviewedPostprin

Assessing the strength of directed influences among neural signals : An approach to noisy data

Acknowledgements This work was supported by the German Science Foundation (Ti315/4-2), the German Federal Ministry of Education and Research (BMBF grant 01GQ0420), and the Excellence Initiative of the German Federal and State Governments. B.S. is indebted to the Kosterlitz Centre for the financial support of this research project.Peer reviewedPreprin

On the validity of neural mass models

FUNDING This study received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement #642563 (COSMOS). ACKNOWLEDGMENTS ND and AD want to thank Rok Cestnik and Bastian Pietras for fruitful discussionsPeer reviewedPublisher PD

Distinguishing Direct from Indirect Interactions in Oscillatory Networks with Multiple Time Scales

Peer reviewedPublisher PD