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

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 …

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 …

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

We introduce here phase transfer entropy (Phase TE) as a measure of directed connectivity
among neuronal oscillations. Phase TE quantifies the transfer entropy between phase time …

The analysis of the interdependence between time series has become an important field of
research in the last years, mainly as a result of advances in the characterization of …

We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian
state-space modeling approach used to infer effective connectivity from noninvasive …

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 …

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-
like dynamics arising from the interplay of their synapse-like electrical junctions and their …