1 code implementation • 28 Jan 2022 • Oliver M. Cliff, Joseph T. Lizier, Naotsugu Tsuchiya, Ben D. Fulcher
Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems.
1 code implementation • 15 Jul 2020 • Leonardo Novelli, Joseph T. Lizier
Functional and effective networks inferred from time series are at the core of network neuroscience.
Neurons and Cognition Information Theory Social and Information Networks Information Theory Data Analysis, Statistics and Probability
1 code implementation • 9 Mar 2020 • Oliver M. Cliff, Leonardo Novelli, Ben D. Fulcher, James M. Shine, Joseph T. Lizier
Inferring linear dependence between time series is central to our understanding of natural and artificial systems.
Methodology Information Theory Information Theory Statistics Theory Data Analysis, Statistics and Probability Neurons and Cognition Applications Statistics Theory
2 code implementations • 7 Nov 2019 • Leonardo Novelli, Fatihcan M. Atay, Jürgen Jost, Joseph T. Lizier
The pairwise (or bivariate) transfer entropy from a source to a target node in a network does not depend solely on the local source-target link weight, but on the wider network structure that the link is embedded in.
Information Theory Social and Information Networks Information Theory Data Analysis, Statistics and Probability Neurons and Cognition
1 code implementation • 14 Aug 2014 • Joseph T. Lizier
We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data.
Information Theory Mathematical Software Social and Information Networks Information Theory Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability 94A15
no code implementations • 30 Jun 2014 • Yu Sun, Louis F. Rossi, Chien-Chung Shen, Jennifer Miller, X. Rosalind Wang, Joseph T. Lizier, Mikhail Prokopenko, Upul Senanayake
Depending upon the leadership model, leaders can use their external information either all the time or in response to local conditions [Couzin et al. 2005; Sun et al. 2013].