JIDT: An information-theoretic toolkit for studying the dynamics of complex systems (1408.3270v2)

Abstract: Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. 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. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments. We present the principles behind the code design, and provide several examples to guide users.

• The paper presents an open-source toolkit that quantifies information storage, transfer, and modification in complex systems.
• It employs methods like transfer entropy and active information storage using Gaussian, box-kernel, and KSG estimators for diverse data types.
• JIDT’s cross-platform design and compatibility with MATLAB, GNU Octave, and Python streamline the analysis of neural, bioinformatics, and artificial life systems.

Overview of JIDT: Toolkit for Information-Theoretic Analysis of Complex Systems

The paper presents the Java Information Dynamics Toolkit (JIDT), an open-source software package designed to facilitate the empirical estimation of information-theoretic measures from time-series data. This toolkit addresses the increasing application of information theory to the paper of complex systems, with a particular focus on computational neuroscience, bioinformatics, and artificial life systems.

Information Dynamics in Complex Systems

Traditionally, information theory, particularly Shannon's measures, has been employed to paper complex systems to gauge fundamental limits on signal processing. These systems consist of many interacting components, where the global behavior emerges from local interactions, akin to flock navigation or neural consciousness. The perspective that complex systems are fundamentally about information processing has led to the application of measures like entropy and mutual information to paper these systems.

JIDT takes this further by concentrating on information dynamics, specifically quantifying information storage, transfer, and modification over space and time. In essence, it examines the distributed computation embodied in complex systems by evaluating how information is transported between elements, how it is retained, and how sources of information interact computationally.

Toolkit Design and Implementation

JIDT is implemented in Java, enabling cross-platform usage due to Java's virtual machine compatibility. It supports various environments such as MATLAB, GNU Octave, and Python. The toolkit provides standalone, licensed under GNU General Public License v3, containing implementations for measures such as transfer entropy, active information storage, predictive information, and more. The measures are estimated using techniques suited for discrete and continuous data, employing Gaussian, box-kernel, and Kraskov-Stögbauer-Grassberger (KSG) estimation approaches.

Key features of JIDT include:

• Standalone application with no requirement for additional installations aside from a JVM.
• Implementation of various measures of information dynamics and their multivariate forms.
• Capability of running in numerous environments, making it accessible across different research contexts.
• Flexibility in estimator types, reflective of the general purpose nature of the toolkit.
• Inclusion of empirical methods for statistical significance tests, allowing users to determine whether computed information-theoretic measures reflect actual relationships rather than being artifacts of sample limitations.

Implications and Future Directions

The capabilities of JIDT make it a valuable resource for researchers analyzing complex systems, particularly in neuroscience where transfer entropy has been influential in effective network inference. By offering robust tools to measure how information is processed and transferred in neural systems and other complex networks, JIDT supports a deeper understanding of the emergent properties in these systems.

Theoretical implications extend to refining our understanding of intrinsic computation within complex systems and how these systems self-organize and adapt. The practical implications include the engineering of more efficient network systems or developing novel computational models reflective of biological systems.

Looking forward, enhancements to JIDT could include support for distributed or parallel computing frameworks to handle large datasets and computational loads inherent to real-world applications. Additionally, incorporating methods for automated parameter selection for time-series embedding could further streamline analyses in JIDT, broadening its application range and reducing user intervention.

Conclusion

JIDT stands out as a comprehensive toolkit for information-theoretic analysis of complex systems, providing researchers with tools to decode the information dynamics within these intricate constructs. Through its diverse range of implemented measures and flexible, environment-agnostic design, JIDT exemplifies a mature approach to studying how information processing underpins emergent phenomena in complex systems.

By consolidating various estimation techniques and ensuring ease of integration into existing research workflows, JIDT empowers researchers to delve into the intricate tapestry of information dynamics that characterize complex systems, fostering advancements across multiple scientific disciplines.