Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Determining Network Structure from Data: Nonlinear Modeling Methods

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_439

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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Further Reading

  1. Hellwig B, Haeussler S, Lauk M, Koester B, Guschlbauer B, Kristeva-Feige R, Timmer J, Luecking CH (2000) Tremor-correlated cortical activity detected by electroencephalography. Electroencephalogr Clin Neurophysiol 111:806–809Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Institute for Complex Systems and Mathematical BiologyUniversity of AberdeenAberdeenUK
  2. 2.Department of PhysicsInstitute for Complex Systems and Mathematical Biology, University of AberdeenAberdeenUK