Analysis of information transfer has found rapid adoption in
neuroscience, where a highly dynamic transfer of information
continuously runs on top of the brain's slowly-changing anatomical
connectivity. Measuring such transfer is crucial to understanding
how flexible information routing and processing give rise to higher
cognitive function. Directed Information Measures in Neuroscience
reviews recent developments of concepts and tools for measuring
information transfer, their application to neurophysiological
recordings and analysis of interactions. Written by the most active
researchers in the field the book discusses the state of the art,
future prospects and challenges on the way to an efficient
assessment of neuronal information transfer. Highlights include the
theoretical quantification and practical estimation of information
transfer, description of transfer locally in space and time,
multivariate directed measures, information decomposition among a
set of stimulus/responses variables and the relation between
interventional and observational causality. Applications to neural
data sets and pointers to open source software highlight the
usefulness of these measures in experimental neuroscience. With
state-of-the-art mathematical developments, computational
techniques and applications to real data sets, this book will be of
benefit to all graduate students and researchers interested in
detecting and understanding the information transfer between
components of complex systems.
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