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 …

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 …

Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and
Huxley, who pioneered quantitative analysis of electrical activity in the nervous system …

Background Transfer entropy (TE) is a measure for the detection of directed interactions.
Transfer entropy is an information theoretic implementation of Wiener's principle of …

Activity-dependent self-organization plays an important role in the formation of specific and
stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and …

Abstract Information transfer is a key component of information processing, next to
information storage and modification. Information transfer can be measured by a variety of …

Information theory allows us to investigate information processing in neural systems in terms
of information transfer, storage and modification. Especially the measure of information …

There has been considerable debate in the literature about the relative merits of information
processing versus dynamical approaches to understanding cognitive processes. In this …

Information Theory started and, according to some, ended with Shannon's seminal paper “A
Mathematical Theory of Communication”(Shannon 1948). Because its significance and …