5 citations · 10 across the 11 of their papers we have counts for
12 papers
Dissecting Spectral Granger Causality through Partial Information Decomposition
Luca Faes, Gorana Mijatovic, Riccardo Pernice +3
Granger causality (GC), a popular statistical method for the inference of directional influences between time series measured from a complex network, is sensitive to high-order (no…
Localizing synergies of hidden factors across complex systems: resting brain networks and HeLa gene expression profile as case studies
Marlis Ontivero-Ortega, Gorana Mijatovic, Luca Faes +2
Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Eve…
A Method for the Time-Frequency Analysis of High-Order Interactions in Non-Stationary Physiological Networks
Yuri Antonacci, Chiara Bara', Laura Sparacino +3
Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions involving three or more components of a network system. Most of…
Predictive Information Decomposition as a Tool to Quantify Emergent Dynamical Behaviors In Physiological Networks
Luca Faes, Gorana Mijatovic, Laura Sparacino +1
Objective: This work introduces a framework for multivariate time series analysis aimed at detecting and quantifying collective emerging behaviors in the dynamics of physiological…
Decomposing Multivariate Information Rates in Networks of Random Processes
Laura Sparacino, Gorana Mijatovic, Yuri Antonacci +4
The Partial Information Decomposition (PID) framework has emerged as a powerful tool for analyzing high-order interdependencies in complex network systems. However, its application…
Partial Information Rate Decomposition
Luca Faes, Laura Sparacino, Gorana Mijatovic +4
Partial Information Decomposition (PID) is a principled and flexible method to unveil complex high-order interactions in multi-unit network systems. Though being defined exclusivel…
A Model-Free Method to Quantify Memory Utilization in Neural Point Processes
Gorana Mijatovic, Sebastiano Stramaglia, Luca Faes
Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neur…
Network Representation of Higher-Order Interactions Based on Information Dynamics
Gorana Mijatovic, Yuri Antonacci, Michal Javorka +3
Many complex systems in science and engineering are modeled as networks whose nodes and links depict the temporal evolution of each system unit and the dynamic interaction between…
Measuring hierarchically-organized interactions in dynamic networks through spectral entropy rates: theory, estimation, and illustrative application to physiological networks
Laura Sparacino, Yuri Antonacci, Gorana Mijatovic +1
Recent advances in signal processing and information theory are boosting the development of new approaches for the data-driven modelling of complex network systems. In the fields o…
Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures
Gorana Mijatovic, Laura Sparacino, Yuri Antonacci +4
The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables.…
A Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Brain and Physiological Networks
Luca Faes, Gorana Mijatovic, Yuri Antonacci +7
While the standard network description of complex systems is based on quantifying links between pairs of system units, higher-order interactions (HOIs) involving three or more unit…
An information-theoretic framework to measure the dynamic interaction between neural spike trains
Gorana Mijatovic, Yuri Antonacci, Tatjana Loncar-Turukalo +2
Understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience. However, an optimal approach of as…