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International Conference on Brain Informatics and Health

BIH 2014: Brain Informatics and Health pp 424–435Cite as

Causality and Influentiability: The Need for Distinct Neural Connectivity Concepts

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8609))

Abstract

We employ toy models to re-examine the notion of causality and its implications in unravelling networks in neuroscience. We conclude that even though multivariate representations of neural dynamic data is indispensable, current popular terminologies for addressing connectivity are insufficiently precise and may even be misleading for fully describing the breadth of information multivariate models now provide. This imposes the need to consider a brand new link centered paradigm of network description where the directed nature of the links plays a central role.

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Author information

Authors and Affiliations

  1. Escola Politécnica, University of São Paulo, São Paulo, SP, 05508-900, Brazil

    Luiz A. Baccalá

  2. Department of Radiology and Oncology, Faculdade de Medicina, University of São Paulo, São Paulo, Brazil

    Koichi Sameshima

Authors
  1. Luiz A. Baccalá
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  2. Koichi Sameshima
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślȩzak

  2. School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore

    Ah-Hwee Tan

  3. Computational Intelligence Laboratory, ECE Department, University of Manitoba, R3T 5V6, Winnipeg, MB, Canada

    James F. Peters

  4. Institute of Computer Science, University of Rostock, Rostock, Germany

    Lars Schwabe

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© 2014 Springer International Publishing Switzerland

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Baccalá, L.A., Sameshima, K. (2014). Causality and Influentiability: The Need for Distinct Neural Connectivity Concepts. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_39

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  • DOI: https://doi.org/10.1007/978-3-319-09891-3_39

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09890-6

  • Online ISBN: 978-3-319-09891-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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