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Artificial Neural Networks (ANN)

  • Lubica Benuskova
  • Nikola Kasabov
Chapter
  • 556 Downloads
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

Abstract

This chapter introduces the basic principles of artificial neural networks (ANN) as computational models that mimic the brain in its main principles. Theyhavebeenused so far to model brain functions, along with solving complex problems of classification, prediction, etc. in all areas of science, engineering, technology and business. Here we present a classification scheme of the different types of ANN and some main existing models, namely self-organized maps (SOM), multilayer-perceptrons (MLP) and spiking neural networks (SNN). We illustrate their use to model brain functions, for instance the generation of electrical oscillations measured as LFP. Since ANNs are used as models of brain functions, they become an integral part of CNGM where gene interactions are introduced as part of the structure andthe functionality of ANN (see e.g. Chap. 8).

Keywords

Artificial Neural Network Fuzzy Rule Problem Space Local Field Potential Goal Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media, LLC 2007

Authors and Affiliations

  • Lubica Benuskova
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAUTAucklandNew Zealand

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