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Determining Network Structure from Data: Nonlinear Modeling Methods

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Encyclopedia of Computational Neuroscience

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Definition

In this entry we focus on inference of network structures from data. One possible approach to studying networks is to model the nodes, such as neurons, and generate networks and run simulations and observe the network behavior. This approach requires on a priori assumptions about the constituent parts; for instance, Hodgkin-Huxley neurons may be coupled and the resulting network behavior is investigated. The model behaviors can be compared to the measured neuronal signals through statistical analysis. However, this approach only provides indirect information about the network structure. An alternative approach to studying network structure is to use parametric, semiparametric, and nonparametric analyses of the observed signals and reconstruct the network connections. Such analyses are essential tools for systems in which network structure is not known, such as when anatomical connections are not known or information is not sufficient.

A particular challenge when inferring...

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Authors and Affiliations

  1. Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Room 319, Meston Building, Meston Walk, AB24 3UE, Aberdeen, Scotland, UK

    Dr. Bjoern Schelter

  2. Department of Physics, Institute for Complex Systems and Mathematical Biology, AB24 3FX, Aberdeen, UK

    Dr. Marco Thiel

Authors
  1. Dr. Bjoern Schelter
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  2. Dr. Marco Thiel
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Correspondence to Bjoern Schelter .

Editor information

Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Schelter, B., Thiel, M. (2014). Determining Network Structure from Data: Nonlinear Modeling Methods. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_439-2

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_439-2

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