Transfer entropy in neuroscience
Abstract Information transfer is a key component of information processing, next to
information storage and modification. Information transfer can be measured by a variety of …
information storage and modification. Information transfer can be measured by a variety of …
Efficient transfer entropy analysis of non-stationary neural time series
Information theory allows us to investigate information processing in neural systems in terms
of information transfer, storage and modification. Especially the measure of information …
of information transfer, storage and modification. Especially the measure of information …
Evaluation of Granger causality measures for constructing networks from multivariate time series
Granger causality and variants of this concept allow the study of complex dynamical systems
as networks constructed from multivariate time series. In this work, a large number of …
as networks constructed from multivariate time series. In this work, a large number of …
The influence of filtering and downsampling on the estimation of transfer entropy
Transfer entropy (TE) provides a generalized and model-free framework to study Wiener-
Granger causality between brain regions. Because of its nonparametric character, TE can …
Granger causality between brain regions. Because of its nonparametric character, TE can …
[HTML][HTML] Inferring Dealer Networks in the Foreign Exchange Market Using Conditional Transfer Entropy: Analysis of a Central Bank Announcement
A Janczewski, I Anagnostou, D Kandhai - Entropy, 2024 - mdpi.com
The foreign exchange (FX) market has evolved into a complex system where locally
generated information percolates through the dealer network via high-frequency …
generated information percolates through the dealer network via high-frequency …
A graph algorithmic approach to separate direct from indirect neural interactions
Network graphs have become a popular tool to represent complex systems composed of
many interacting subunits; especially in neuroscience, network graphs are increasingly used …
many interacting subunits; especially in neuroscience, network graphs are increasingly used …
Improving on transfer entropy-based network reconstruction using time-delays: Approach and validation
Transfer entropy constitutes a viable model-free tool to infer causal relationships between
two dynamical systems from their time-series. In an information-theoretic sense, transfer …
two dynamical systems from their time-series. In an information-theoretic sense, transfer …
Causality networks from multivariate time series and application to epilepsy
E Siggiridou, C Koutlis, A Tsimpiris… - 2015 37th annual …, 2015 - ieeexplore.ieee.org
Granger causality and variants of this concept allow the study of complex dynamical systems
as networks constructed from multivariate time series. In this work, a large number of …
as networks constructed from multivariate time series. In this work, a large number of …
Brain network analysis based on frontal cortical EEG sources in major depressive disorder (MDD)
N Amirkhan, F Hasanzadeh… - 2021 28th National …, 2021 - ieeexplore.ieee.org
Studies on functional connectivity and brain networks have suggested that major depressive
disorder (MDD) affects brain networks. This paper investigates the abnormalities in MDD …
disorder (MDD) affects brain networks. This paper investigates the abnormalities in MDD …