A celebrated and controversial hypothesis suggests that some biological systems—parts,
aspects, or groups of them—may extract important functional benefits from operating at the …

Understanding how neural systems integrate, encode, and compute information is central to
understanding brain function. Frequently, data from neuroscience experiments are …

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 …

Understanding causal relationships, or effective connectivity, between parts of the brain is of
utmost importance because a large part of the brain's activity is thought to be internally …

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

Shannon information theory provides various measures of so-called syntactic information,
which reflect the amount of statistical correlation between systems. By contrast, the concept …

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 …

With the advent of the Big Data era and the development of machine learning technologies,
predicting stock movements by analyzing news articles, which are unstructured data, has …

We investigate information processing in randomly connected recurrent neural networks. It
has been shown previously that the computational capabilities of these networks are …

This paper empirically investigates whether cryptocurrencies might have a useful role in
financial modelling and risk management in the energy markets. To do so, the causal …