Directed Information Measures in Neuroscience

  • Michael Wibral
  • Raul Vicente
  • Joseph T. Lizier

Part of the Understanding Complex Systems book series (UCS)

Table of contents

  1. Front Matter
    Pages 1-12
  2. Introduction to Directed Information Measures

    1. Front Matter
      Pages 1-2
    2. Michael Wibral, Raul Vicente, Michael Lindner
      Pages 3-36
    3. Raul Vicente, Michael Wibral
      Pages 37-58
  3. Information Transfer in Neural and Other Physiological Systems

    1. Front Matter
      Pages 59-60
    2. Daniele Marinazzo, Guorong Wu, Mario Pellicoro, Sebastiano Stramaglia
      Pages 87-110
    3. Vasily A. Vakorin, Olga Krakovska, Anthony R. McIntosh
      Pages 137-158
  4. Recent Advances in the Analysis of Information Processing

  5. Back Matter
    Pages 221-224

About this book

Introduction

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.

Keywords

Brain connectivity Causality in neuroscience EEG data Effective connectivity Granger causality Information theory in computational neuroscience Information transfer in networks Model free measures Neural information processing Transfer entropy

Editors and affiliations

  • Michael Wibral
    • 1
  • Raul Vicente
    • 2
  • Joseph T. Lizier
    • 3
  1. 1.Brain Imaging CenterFrankfurt am MainGermany
  2. 2.Max-Planck Institute for Brain ResearchFrankfurt am MainGermany
  3. 3.CSIRO Computational InformaticsMarsfield, SydneyAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-54474-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-54473-6
  • Online ISBN 978-3-642-54474-3
  • Series Print ISSN 1860-0832
  • Series Online ISSN 1860-0840
  • About this book