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Encyclopedia of Computational Neuroscience pp 1003–1016Cite as

Determining Network Structure from Data: Nonlinear Modeling Methods

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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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Author information

Authors and Affiliations

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

    Bjoern Schelter

  2. Department of Physics, Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK

    Marco Thiel

Authors
  1. Bjoern Schelter
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  2. Marco Thiel
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Corresponding author

Correspondence to Bjoern Schelter .

Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, Atlanta, GA, USA

    Dieter Jaeger

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

    Ranu Jung

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Schelter, B., Thiel, M. (2015). 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/10.1007/978-1-4614-6675-8_439

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