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Graphical analyses in delay interaction networks

Patricia Wollstadt, Raul Vicente, Michael Wibral
2013 BMC Neuroscience  
We demonstrate how both questions can be addressed using a modified estimator of transfer entropy (TE).  ...  Using a modified TE estimator that uses delayed states of the driving system, one can mathematically prove that transfer entropy values peak if the delay of the state of the driving system equals the true  ...  We demonstrate how both questions can be addressed using a modified estimator of transfer entropy (TE).  ... 
doi:10.1186/1471-2202-14-s1-p413 fatcat:upvrpheuujhhddp7bzfhsuipmq

Symbolic Information Flow Measurement (SIFM): A software for measurement of information flow using symbolic analysis

Dhurata Nebiu, Hiqmet Kamberaj
2020 SoftwareX  
Symbolic Information Flow Measurement software is used to compute the information flow between different components of a dynamical system or different dynamical systems using symbolic transfer entropy.  ...  Information flow is measured in terms of the local and average symbolic transfer entropies. We also introduce a new measure of mutual information based on the symbolic analysis.  ...  They are often determined using the principal components analysis (PCA) [9] as for biomolecules [10, 11] .  ... 
doi:10.1016/j.softx.2020.100470 fatcat:ohklhcehbjbihhkddglg3pvuxe

A network physiology approach to oxygen saturation variability during normobaric hypoxia

Yuji Jiang, Joseph T. Costello, Thomas B. Williams, Nawamin Panyapiean, Amar Bhogal, Michael J. Tipton, Jo Corbett, Ali R. Mani
2020 Experimental Physiology  
Method we explored the use of transfer entropy in order to compute the flow of information between cardio-respiratory signals during hypoxia.  ...  The transfer entropy analysis showed that, particularly at FI O2 : 0.145 and 0.12, the flow of information between SpO2 and other physiological variables exhibits a bidirectional relationship.  ...  This matrix was used to plot a directed graph. The codes for calculation of transfer entropy and plotting the network were written in MATLAB.  ... 
doi:10.1113/ep088755 pmid:32643311 fatcat:hvp6dfymxzbc7gwfveziicawwu

Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?

Ali Tehrani-Saleh, Christoph Adami
2020 Entropy  
Because it is unclear how often such cryptic influences emerge in cognitive systems, the usefulness of transfer entropy measures to reconstruct information flow is unknown.  ...  However, recent work has shown that directed information measures such as transfer entropy can sometimes inadequately estimate information flow, or even fail to identify manifest directed influences, especially  ...  Abbreviations The following abbreviations are used in this manuscript: MB  ... 
doi:10.3390/e22040385 pmid:33286159 fatcat:toikqzm6rjbcbkqqktvl7hilce

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

Jaroslav Hlinka, David Hartman, Martin Vejmelka, Jakob Runge, Norbert Marwan, Jürgen Kurths, Milan Paluš
2013 Entropy  
We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surface air temperature data from  ...  For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods.  ...  of transfer entropy.  ... 
doi:10.3390/e15062023 fatcat:4psfolrbxbbx3gfqm2mn7x3dbi

Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing? [article]

Ali Tehrani-Saleh, Christoph Adami
2019 arXiv   pre-print
We also test whether activity time-series recorded from behaving digital brains can infer information flow using the transfer entropy concept, when compared to a ground-truth model of causal influence  ...  Our results suggest that transfer entropy will sometimes fail to infer causality when it exists, and sometimes suggest a causal connection when there is none.  ...  Abbreviations The following abbreviations are used in this manuscript: MB  ... 
arXiv:1901.07589v2 fatcat:wp7voc6zirdupfjlng5cntpmfm

Information-theoretic assessment of cardiovascular-brain networks during sleep

Luca Faes, Daniele Marinazzo, Giandomenico Nollo
2015 2015 Computing in Cardiology Conference (CinC)  
Both approaches consistently detected structured networks of physiological interactions, revealing (i) strong internal information in all systems; (ii) information transfer directed predominantly from  ...  Then, the causal statistical dependencies within and between these six time series were assessed in terms of internal information (conditional self entropy, CSE) and information transfer (transfer entropy  ...  the transfer entropy (TE) [6] .  ... 
doi:10.1109/cic.2015.7410988 dblp:conf/cinc/FaesMN15 fatcat:hoinp2brpnehtlg46m27t76lsm

Complexity Evaluation of an Environmental Control and Life-Support System Based on Directed and Undirected Structural Entropy Methods

Kaichun Yang, Chunxin Yang, Han Yang, Chenglong Zhou
2021 Entropy  
In this study, we use the structural entropy method (SEM) to calculate the system order degree to quantitatively evaluate the ECLSS complexity at the top level.  ...  The complexity of the system evaluated by directed SEM and undirected SEM presents different rules.  ...  Figure 5 of 1 Figure 5 . 515 depicts the subsystem components of top-level scheme I and material transfer relationships among the components.  ... 
doi:10.3390/e23091173 pmid:34573798 fatcat:asa7uh6gnrhidetdt7m6yxjgdi

Modulation of brain connectivity by memory load in a working memory network

Pouya Bashivan, Mohammed Yeasin, Gavin M. Bidelman
2014 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)  
An information theoretic metric called transfer entropy was adopted to examine the directed links across these areas.  ...  Independent component analysis (ICA) was used to identify brain areas, active during a working memory task, whose activations co-varied with memory load.  ...  the transfer entropy.  ... 
doi:10.1109/ccmb.2014.7020705 fatcat:goblnexqzvcnpmflikadkzckma

Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

Tjeerd W. Boonstra, Luca Faes, Jennifer N. Kerkman, Daniele Marinazzo
2019 NeuroImage  
Conditional mutual information and transfer entropy revealed sparse networks dominated by local connections between muscles.  ...  We used network analysis to quantify the structure of functional interactions between muscles and compared them across experimental conditions.  ...  The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, FWO and the Flemish  ... 
doi:10.1016/j.neuroimage.2019.116093 pmid:31404629 fatcat:suxzi3m7arh3fh6oufnt3ss2di

Information dynamics of brain–heart physiological networks during sleep

L Faes, G Nollo, F Jurysta, D Marinazzo
2014 New Journal of Physics  
This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep.  ...  Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred  ...  The direct transfer is useful to infer the structure of the network of interacting processes.  ... 
doi:10.1088/1367-2630/16/10/105005 fatcat:qnj7ccvglrdtrjg5t6hdod5coq

Spectral Ranking of Causal Influence in Complex Systems

Errol Zalmijn, Tom Heskes, Tom Claassen
2021 Entropy  
To reliably narrow down the causal search space, we validate a ranking algorithm that applies transfer entropy for bivariate interaction analysis of a system's multivariate time series to obtain a weighted  ...  The results suggest that this approach robustly identifies the true drivers or causes of a complex system's deviant behavior, even when its reconstructed information transfer network includes redundant  ...  Therefore, multivariate approaches use conditional transfer entropy [4] to separate true cause-effect relations from mere correlations, i.e., direct from so-called transitive indirect or semi-metric,  ... 
doi:10.3390/e23030369 pmid:33804599 pmcid:PMC8003657 fatcat:kz6mov7r5zeybgr7kvgur73xsa

A Study on the Information Sharing in Supply Chain Network Based on Information Entropy

Nyunho JUNG, Masanobu MATSUMARU
2013 Journal of Japan Industrial Management Association  
Secondly, based on the previous model of increasing the sum of load allocation entropy and information transfer entropy, this study extends the issue to information sharing in supply chain network.  ...  In addition, we attempt to analyze the proposed model using simple numerical examples.  ...  [11] presented a perspective of "information transfer entropy" H and "load distribution entropy" I based on the model of a communication network as outlined in section 2.  ... 
doi:10.11221/jima.64.317 fatcat:hzboohbexrf7bhayg3pt4meypi

Information spillover across international real estate investment trusts: Evidence from an entropy-based network analysis

Qiang Ji, Hardik Marfatia, Rangan Gupta
2018 The North American journal of economics and finance  
For a network of nine leading industrial economies, we unveil static and time-varying information spillover of REIT returns using total transfer entropy, pairwise net transfer entropy and directional (  ...  The extreme value analysis shows the changing role of US and UK housing markets. JEL Codes: R30, R33, G14.  ...  transfer entropy, pairwise net transfer entropy and directional ("From" and "To") transfer entropy.  ... 
doi:10.1016/j.najef.2018.04.002 fatcat:jchg4i7xrjhoxguz25usr3c5oa

Large-scale augmented Granger causality (lsAGC) for connectivity analysis in complex systems: from computer simulations to functional MRI (fMRI)

Axel Wismüller, M. Ali Vosoughi, Barjor S. Gimi, Andrzej Krol
2021 Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging  
Our results suggest that lsAGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such  ...  We quantitatively evaluate the performance of lsAGC on synthetic directional time-series networks with known ground truth.  ...  Various methods have been proposed to obtain directional relationships in multivariate time-series data, e.g., transfer entropy [1] and mutual information [2] .  ... 
doi:10.1117/12.2582152 fatcat:c7b2t3rc6feyxezvtgzkfsemla
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