CNS*2020 software showcase

Information theory and directed network inference (using JIDT and IDTxl)

Software showcase at CNS*2020, to be held online on July 18 11pm Berlin time (July 19 7am Sydney time), 2020 (description of this tutorial at CNS*2020)

To attend, please register to CNS*2020 (free!)

Presenters: Leonardo Novelli, Joseph Lizier

Description of the showcase

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 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 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.

Software tools:

• JIDT: https://github.com/jlizier/jidt
• IDTxl: https://github.com/pwollstadt/IDTxl

Links to resources

1. Slides for the showcase
2. Course: Information theory and complex systems (alpha version) --
   • homepage
   • slides
   • videos
3. Google groups for support:
   • JIDT google group
   • IDTxl google group
4. Related Workshop on Methods of Information Theory in Computational Neuroscience at CNS*2020

Background reading:

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