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Saturday, July 18 • 11:00pm - 11:30pm
Information theory and directed network inference (using JIDT and IDTxl)

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Leonardo Novelli, Joseph T. Lizier 

Tutorial Website (with additional resources, links to slides etc)

S1: Information theoretic measures including transfer entropy are widely used to analyse neuroimaging time series and to infer directed connectivity [1]. The JIDT [2] and IDTxl [3] software toolkits provide efficient measures and algorithms for these applications:
  • JIDT (https://github.com/jlizier/jidt) provides a fundamental computation engine for efficient estimation of information theoretic measures for a variety of applications. It can be easily used in Matlab, Python, and Java, and provides a GUI interface for push-button analysis and code template generation.
  • IDTxl (https://github.com/pwollstadt/IDTxl) is a specific Python toolkit for directed network inference in neuroscience. It employs multivariate transfer entropy and hierarchical statistical tests to control false positives and has been validated at realistic scales for neural data sets [4]. The inference can be run in parallel using GPUs or a high-performance computing cluster.
This tutorial session will help you get started with software analyses via brief overviews of the toolkits and demonstrations.

Tutorial Website

Neurostars forum for Q&A

References
  1. Wibral, M., Vicente, R., & Lizier, J. T. (2014). Directed Information Measures in Neuroscience. Springer, Berlin. https://doi.org/10.1007/978-3-642-54474-3
  2. Lizier, J. T. (2014). JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems. Frontiers in Robotics and AI, 1, 11. https://doi.org/10.3389/frobt.2014.00011
  3. Wollstadt, P., Lizier, J. T., Vicente, R., Finn, C., Martinez-Zarzuela, M., Mediano, P., Novelli, L., and Wibral, M. (2019). IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks. Journal of Open Source Software, 4(34), 1081. https://doi.org/10.21105/joss.01081
  4. Novelli, L., Wollstadt, P., Mediano, P., Wibral, M., & Lizier, J. T. (2019). Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing. Network Neuroscience, 3(3), 827–847. https://doi.org/10.1162/netn_a_00092
Link to software tools:

Speakers
avatar for Joseph Lizier

Joseph Lizier

Associate Professor, Centre for Complex Systems, The University of Sydney
My research focusses on studying the dynamics of information processing in biological and bio-inspired complex systems and networks, using tools from information theory such as transfer entropy to reveal when and where in a complex system information is being stored, transferred and... Read More →
avatar for Leonardo Novelli

Leonardo Novelli

PhD Student, Centre for Complex Systems, The University of Sydney


Saturday July 18, 2020 11:00pm - 11:30pm CEST
Crowdcast