A short introduction to JIDT: An information-theoretic toolkit for studying the dynamics of complex systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in Artificial Life, computational neuroscience and bioinformatics. This trend has resulted in a strong uptake of information-theoretic measures to analyse the dynamics of complex systems in these fields. This talk will briefly review the use of these measures as applied to complex systems, and then introduce a software toolkit for conducting such analysis -- the Java information dynamics toolkit (JIDT). JIDT provides a standalone, open-source code implementation of measures for information dynamics, i.e. measures to quantify information storage, transfer and modification, and the dynamics of these operations in space and time. Principally, the toolkit implements the transfer entropy, (conditional) mutual information and active information storage, for both discrete and continuous-valued data. Various types of estimator (e.g. Gaussian, Kraskov-Stoegbauer-Grassberger) are provided for each measure. Furthermore, while written in Java, the toolkit can be used directly in Matlab, Octave and Python. I will describe how to install the JIDT software, how to get started with typical usage scenarios, and where to seek further support information. We will describe how to get started analysing your own data sets, as well as showcasing more complex demonstrations, e.g. analysing information dynamics in cellular automata.