What is a pattern? How do we come to recognize patterns never seen before? Quantifying
the notion of pattern and formalizing the process of pattern discovery go right to the heart of …

Inspiration for artificial biologically inspired computing is often drawn from neural systems.
This article shows how to analyze neural systems using information theory with the aim of …

Transfer Entropy Page 1 Chapter 4 Transfer Entropy In this chapter we get to the essential
mathematics of the book—a detailed discussion of transfer entropy. To begin with we look at …

Complex systems are increasingly being viewed as distributed information processing
systems, particularly in the domains of computational neuroscience, bioinformatics, and …

In complex networks such as gene networks, traffic systems or brain circuits it is important to
understand how long it takes for the different parts of the network to effectively influence one …

To fully characterize the information that two source variables carry about a third target
variable, one must decompose the total information into redundant, unique, and synergistic …

Information storage is a key component of intrinsic distributed computation. Despite the
existence of appropriate measures for it (eg excess entropy), its role in interacting with …

Network inference algorithms are valuable tools for the study of large-scale neuroimaging
datasets. Multivariate transfer entropy is well suited for this task, being a model-free measure …

The apparent dichotomy between information-processing and dynamical approaches to
complexity science forces researchers to choose between two diverging sets of tools and …

Causality lies at the heart of scientific inquiry, serving as the fundamental basis for
understanding interactions among variables in physical systems. Despite its central role …