22 citations · 32 across the 12 of their papers we have counts for
5 papers · 1 filter
Assessing high-order effects in feature importance via predictability decomposition
Marlis Ontivero-Ortega, Luca Faes, Jesus M Cortes +2
Leveraging the large body of work devoted in recent years to describe redundancy and synergy in multivariate interactions among random variables, we propose a novel approach to qua…
Measures and Models of Brain-Heart Interactions
Diego Candia-Rivera, Luca Faes, Fabrizio De Vico Fallani +1
The exploration of brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, stands as a significant…
Partial information decomposition for mixed discrete and continuous random variables
Chiara Barà, Yuri Antonacci, Marta Iovino +2
The framework of Partial Information Decomposition (PID) unveils complex nonlinear interactions in network systems by dissecting the mutual information (MI) between a target variab…
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…