mirror of https://github.com/jlizier/jidt
161 lines
14 KiB
Markdown
161 lines
14 KiB
Markdown
# Java Information Dynamics Toolkit (JIDT)
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Copyright (C) 2012- [Joseph T. Lizier](http://lizier.me/joseph/); 2014- Ipek Özdemir; 2017- [Pedro Mediano](https://www.doc.ic.ac.uk/~pam213/); 2019- Emanuele Crosato, Sooraj Sekhar, Oscar Huaigu Xu; 2022- [David Shorten](https://scholar.google.com/citations?user=ggF3Lt4AAAAJ&hl=en)
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*JIDT* provides a stand-alone, open-source code Java implementation (also usable in [Matlab, Octave](../../wiki/UseInOctaveMatlab), [Python](../../wiki/UseInPython), [R](../../wiki/UseInR), [Julia](../../wiki/UseInJulia) and [Clojure](../../wiki/UseInClojure)) of information-theoretic measures of distributed computation in complex systems: i.e. information storage, transfer and modification.
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JIDT includes implementations:
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* principally for the measures **transfer entropy**, **mutual information**, and their conditional variants, as well as **active information storage**, entropy, etc;
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* for both _discrete_ and _continuous_-valued data;
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* using various types of estimators (e.g. _Kraskov-Stögbauer-Grassberger estimators_, _box-kernel estimation_, _linear-Gaussian_),
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as described in full at [ImplementedMeasures](../../wiki/ImplementedMeasures).
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JIDT is easy to use:
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* It ships with a **GUI application** -- the [AutoAnalyser](../../wiki/AutoAnalyser), see picture below -- to facilitate point-and-click analysis, as well as code template generation for more complex analysis.
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* We provide **short video lectures** and corresponding slides in a (beta) [Course](../../wiki/Course) on how to understand using information-theoretic tools to analyse complex systems, and to implement such analysis with JIDT.
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JIDT is distributed under the [GNU GPL v3 license](http://www.gnu.org/licenses/gpl.html) (or later).
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# Getting started
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1. [Download](../../wiki/Downloads) and [Installation](../../wiki/Installation) is very easy!
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1. _Quick start_: take a `git clone` (then build via [AntScripts](../../wiki/AntScripts)) OR download the latest [v1.6.1 full distribution](https://lizier.me/joseph/software/jidt/download.php?file=infodynamics-dist-1.6.1.zip) (suitable for all platforms) and see the readme.txt file therein.
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1. [Documentation](../../wiki/Documentation) including: the paper describing JIDT at [arXiv:1408.3270](http://arxiv.org/abs/1408.3270) (distributed with the toolkit), a (beta) [Course](../../wiki/Course) including short video lectures and a shorter [Tutorial](../../wiki/Tutorial), and [Javadocs (v1.6.1 here)](https://lizier.me/joseph/software/jidt/javadocs/v1.6.1/);
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1. [Demos](../../wiki/Demos) are included with the full distribution, including a [GUI app](../../wiki/AutoAnalyser) for automatic analysis and code generation (see picture below), [simple java demos](../../wiki/SimpleJavaExamples) and [cellular automata (CA) demos](../../wiki/CellularAutomataDemos).
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1. These Java tools can easily be used in [Matlab/Octave](../../wiki/OctaveMatlabExamples), [Python](../../wiki/PythonExamples), [R](../../wiki/R_Examples), [Julia](../../wiki/JuliaExamples) and [Clojure](../../wiki/Clojure_Examples)! (click on each language here for examples)
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[](../../wiki/AutoAnalyser)
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[](../../wiki/Course)
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For further information or announcements:
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* Join our discussion group: http://groups.google.com/d/forum/jidt-discuss
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* See also the [FAQs](../../wiki/FAQs)
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* Follow [@infodynamicstkt](http://twitter.com/infodynamicstkt) on twitter
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# Citation
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Please **cite** your use of this toolkit as:
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Joseph T. Lizier, "JIDT: An information-theoretic toolkit for studying the dynamics of complex systems", _Frontiers in Robotics and AI_ 1:11, 2014; doi:[10.3389/frobt.2014.00011](http://dx.doi.org/10.3389/frobt.2014.00011) (pre-print: [arXiv:1408.3270](http://arxiv.org/abs/1408.3270))
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And please [let me know](mailto:joseph.lizier_AT_gmail.com) about any publications resulting from its use!
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See other [PublicationsUsingThisToolkit](../../wiki/PublicationsUsingThisToolkit).
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# News
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_22/08/2023_ - New full distribution files available for **release v1.6.1**; Changes for v1.6.1 include:
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Minor updates to supporting use in Python, including virtual environments;
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Minor tweaks to fish schooling examples (mostly comments).
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_5/09/2022_ - New full distribution files available for **release v1.6**; Changes for v1.6 include:
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Adding Flocking/Schooling/Swarming demo;
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Included Pedro's code on IIT and O-/S-Information measures;
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Spiking TE estimator added from David;
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Fixed up AutoAnalyser to work well for Python3 and numpy;
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Links to lecture videos included in the beta wiki for the course;
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Added rudimentary effective network inference (simplified version of the IDTxl full algorithm) in demos/octave/EffectiveNetworkInference;
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_26/11/2018_ - New jar and full distribution files available for **release v1.5**; Changes for v1.5 include:
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Added GPU (cuda) capability for KSG Conditional Mutual Information calculator (proper documentation to come), brief [wiki page](../../wiki/GPU) and unit tests included;
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Added auto-embedding for TE/AIS with multivariate KSG, and univariate and multivariate Gaussian estimator (plus unit tests), for Ragwitz criteria and Maximum bias-corrected AIS, and also added Maximum bias corrected AIS and TE to handle source embedding as well;
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Kozachenko entropy estimator adds noise to data by default;
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Added bias-correction property to Gaussian and Kernel estimators for MI and conditional MI, including with surrogates (only option for kernel);
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Enabled use of different bases for different variables in MI discrete estimator;
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All new above features enabled in AutoAnalyser;
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Added drop-down menus for parameters in AutoAnalyser;
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Included long-form lecture slides in course folder;
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_26/11/2017_ - New jar and full distribution files available for **release v1.4**; Changes for v1.4 include:
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Major expansion of functionality for AutoAnalysers: adding Launcher applet and capability to double click jar to launch, added Entropy, CMI, CTE and AIS AutoAnalysers, also added binned estimator type, added all variables/pairs analysis, added statistical significance analysis, and ensured functionality of generated Python code with Python3;
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Added GPU (cuda) capability for KSG Mutual Information calculator (proper documentation and wiki page to come), including unit tests;
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Added fast neighbour search implementations for mixed discrete-continuous KSG MI estimators;
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Expanded Gaussian estimator for multi-information (integration);
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Made all demo/data files readable by Matlab.
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_17/12/2016_ - New book out from J. Lizier et al., ["An Introduction to Transfer Entropy: Information Flow in Complex Systems"](http://bit.ly/te-book-2016) published by Springer, which contains various examples using JIDT (distributed in our releases)
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_21/10/2016_ - New jar and full distribution files available for **release v1.3.1**; Changes for v1.3.1 include:
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Major update to TransferEntropyCalculatorDiscrete so as to implement arbitrary source and dest embeddings and source-dest delay;
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Conditional TE calculators (continuous) handle empty conditional variables;
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Added new auto-embedding method for AIS and TE which maximises bias corrected AIS;
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Added getNumSeparateObservations() method to TE calculators to make reconstructing/separating local values easier after multiple addObservations() calls;
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Fixed kernel estimator classes to return proper densities, not probabilities;
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Bug fix in mixed discrete-continuous MI (Kraskov) implementation;
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Added simple interface for adding joint observations for MultiInfoCalculatorDiscrete
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Including compiled class files for the AutoAnalyser demo in distribution;
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Updated Python demo 1 to show use of numpy arrays with ints;
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Added Python demo 7 and 9 for TE Kraskov with ensemble method and auto-embedding respectively;
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Added Matlab/Octave example 10 for conditional TE via Kraskov (KSG) algorithm;
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Added utilities to prepare for enhancing surrogate calculations with fast nearest neighbour search;
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Minor bug patch to Python readFloatsFile utility.
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_19/7/2015_ - New jar and full distribution files available for **release v1.3**; Changes for v1.3 include:
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Added AutoAnalyser (Code Generator) GUI demo for MI and TE;
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Added auto-embedding capability via Ragwitz criteria for AIS and TE calculators (KSG estimators);
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Added Java demo 9 for showcasing use of Ragwitz auto-embedding;
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Adding small amount of noise to data in all KSG estimators now by default (may be disabled via setProperty());
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Added getProperty() methods for all conditional MI and TE calculators;
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Upgraded Python demos for Python 3 compatibility;
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Fixed bias correction on mixed discrete-continuous KSG calculators;
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Updated the tutorial slides to those in use for ECAL 2015 JIDT tutorial.
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_12/2/2015_ - New jar and full distribution files available for **release v1.2.1**; Changes for v1.2.1 include:
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Added tutorial slides, description of exercises and sample exercise solutions;
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Made jar target Java 1.6;
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Added Schreiber TE heart-breath rate with KSG estimator demo code for Python.
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_28/1/2015_ - New jar and full distribution files available for **release v1.2**; Changes for v1.2 include:
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Dynamic correlation exclusion, or Theiler window, added to all Kraskov estimators;
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Added univariate MI calculation to simple demo 6;
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Added Java code for Schreiber TE heart-breath rate with KSG estimator, ready for use as a template in Tutorial;
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Patch for crashes in KSG conditional MI algorithm 2.
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_20/11/2014_ - New jar and full distribution files available for **release v1.1**; Changes for v1.1 include:
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Implemented Fast Nearest Neighbour Search for Kraskov-Stögbauer-Grassberger (KSG) estimators for MI, conditional MI, TE, conditional TE, AIS, Predictive info, and multi-information. This includes a general (multivariate) k-d tree implementation;
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Added multi-threading (using all available processors by default) for the KSG estimators -- code contributed by Ipek Özdemir;
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Added Predictive information / Excess entropy implementations for KSG, kernel and Gaussian estimators;
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Added R, Julia, and Clojure demos;
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Added Windows batch files for the Simple Java Demos;
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Added property for adding a small amount of noise to data in all KSG estimators;
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_15/8/2014_ JIDT paper finalised and uploaded to the website and [arXiv:1408.3270](http://arxiv.org/abs/1408.3270)
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_14/8/2014_ - New jar and full distribution files available for our **first official release, v1.0**; Changes for v1.0 include: Added the draft of the paper on the toolkit to the release;
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Javadocs made ready for release;
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Switched source->destination arguments for discrete TE calculators to be with source first in line with continuous calculators;
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Renamed all discrete calculators to have Discrete suffix -- TE and conditional TE calculators also renamed to remove "Apparent" prefix and change "Complete" to "Conditional";
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Kraskov estimators now using 4 nearest neighbours by default;
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Unit test for Gaussian TE against ChaLearn Granger causality measurement;
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Added Schreiber TE demos; Interregional transfer demos; documentation for Interaction lag demos; added examples 7 and 8 to Simple Java demos;
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Added property to add noise to data for Kraskov MI;
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Added derivation of Apache Commons Math code for chi square distribution, and included relevant notices in our release;
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Inserted translation class for arrays between Octave and Java;
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Added analytic statistical significance calculation to Gaussian calculators and discrete TE;
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Corrected Kraskov algorithm 2 for conditional MI to follow equation in Wibral et al. 2014.
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_20/4/2014_ - New jar and full distribution files available for v0.2.0; Moved downloads to http://lizier.me/joseph/ since google code has stopped the download facility here :(. Changes for v0.2.0 include: Rearchitected (most) Transfer Entropy and Multivariate TE calculators to use an underlying conditional mutual information calculator, and have arbitrary embedding delay, source-dest delay; this includes moving Kraskov-Grassberger Transfer Entropy calculator to use a single conditional mutual information estimator instead of two mutual information estimators; Rearchitected (most) Active Information Storage calculators to use an underlying mutual information calculator; Added Conditional Transfer Entropy calculators using underlying conditional mutual information calculators; Moved mixed discrete-continuous calculators to a new "mixed" package; bug fixes.
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_11/9/2013_ - New jar and full distribution files available for v0.1.4; added scripts to generate CA figures for 2013 book chapters; added general Java demo code; added Python demo code; made Octave/Matlab demos and CA demos properly compatible for Matlab; added extra Octave/Matlab general demos; added more unit tests for MI and conditional MI calculators, including against results from Wibral's TRENTOOL; bug fixes.
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_11/9/2013_ - New CA demo scripts for several review book chapters we're preparing in 2013 have been uploaded - see [CellularAutomataDemos](../../wiki/CellularAutomataDemos).
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_4/6/2013_ - Added instructions on how to [use in python](../../wiki/UseInPython) and several [PythonExamples](../../wiki/PythonExamples).
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_13/01/2013_ - New jar and full distribution files available for v0.1.3; existing Octave/Matlab demo code made compatible with Matlab; several bug fixes, including using max norm by default in Kraskov calculator (instead of requiring this to be set explicitly); more unit tests (including against results from Kraskov's own MI implementation)
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_19/11/2012_ - New jar and full distribution files available for v0.1.2, including demo code for two newly submitted papers
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_31/10/2012_ - Jar and full distribution files available for v0.1.1 (first distribution)
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_7/5/2012_ - JIDT project created and code uploaded
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# Acknowledgements
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This project has been supported by funding through:
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* Australian Research Council Discovery Early Career Researcher Award (DECRA) "Relating function of complex networks to structure using information theory", J.T. Lizier, 2016-19 DE160100630
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* Universities Australia - Deutscher Akademischer Austauschdienst (German Academic Exchange Service) UA-DAAD Australia-Germany Joint Research Co-operation grant "Measuring neural information synthesis and its impairment", Wibral, Lizier, Priesemann, Wollstadt, Finn, 2016-17
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* University of Sydney Research Accelerator (SOAR) Fellowship 2019 Scheme, J.T. Lizier (CI), 2019-2020
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* Australian Research Council Discovery Project "Large-scale computational modelling of epidemics in Australia: analysis, prediction and mitigation", M. Prokopenko, P. Pattison, M. Gambhir, J.T. Lizier, M. Piraveenan, 2016-19 DP160102742
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