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Found 8 papers, 5 papers with code
Higher-order mutual information reveals synergistic sub-networks for multi-neuron importance
Quantifying which neurons are important with respect to the classification decision of a trained neural network is essential for understanding their inner workings.
Pairwise and high-order dependencies in the cryptocurrency trading network
We first define a cryptocurrency trading network, i. e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as links, later we analyse its evolution over time.
Local Granger Causality
Granger causality is a statistical notion of causal influence based on prediction via vector autoregression.
Quantifying dynamical high-order interdependencies from the O-information: an application to neural spiking dynamics
We address the problem of efficiently and informatively quantifying how multiplets of variables carry information about the future of the dynamical system they belong to.
Synergy as a warning sign of transitions: the case of the two-dimensional Ising model
We consider the formalism of information decomposition of target effects from multi-source interactions, i. e. the problem of defining unique, redundant (or shared), and synergistic (or complementary) components of the information that a set of source variables provides about a target, and apply it to the two-dimensional Ising model as a paradigm of a critically transitioning system.
Statistical Mechanics
Multiscale Granger causality analysis by à trous wavelet transform
Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach.
Kernel Granger causality and the analysis of dynamical networks
We apply the proposed approach to a network of chaotic maps and to a simulated genetic regulatory network: it is shown that the underlying topology of the network can be reconstructed from time series of node's dynamics, provided that a sufficient number of samples is available.
Disordered Systems and Neural Networks Exactly Solvable and Integrable Systems Quantitative Methods
Kernel method for nonlinear Granger causality
Important information on the structure of complex systems, consisting of more than one component, can be obtained by measuring to which extent the individual components exchange information among each other.
Disordered Systems and Neural Networks Exactly Solvable and Integrable Systems
