Abstract Randomized Neural Networks explore the behavior of neural systems where the
majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical …

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 …

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 …

The neurobiological mechanisms of arousal and anesthesia remain poorly understood.
Recent evidence highlights the key role of interactions between the cerebral cortex and the …

In this paper we propose a new image randomness measure using Shannon entropy over
local image blocks. The proposed local Shannon entropy measure overcomes several …

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

Entropy measures are widely applied to quantify the complexity of dynamical systems in
diverse fields. However, the practical application of entropy methods is challenging, due to …

The human brain displays a rich repertoire of states that emerge from the microscopic
interactions of cortical and subcortical neurons. Difficulties inherent within large-scale …

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 …

The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for
efficient inference of networks and their node dynamics from multivariate time series data …