Classification of fMRI Data in the NeuCube Evolving Spiking Neural Network Architecture

  • Norhanifah Murli
  • Nikola Kasabov
  • Bana Handaga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8834)

Abstract

This paper presents a new method and a case study on fMRI spatio- and spectro-temporal data (SSTD) classification with the use of the recently proposed NeuCube architecture [1]. NeuCube is a three dimensional brain-like model of evolving spiking neurons that can be trained with SSTD such as fMRI, EEG and other brain data. This SSTD is mapped, analyzed, modeled and trained, and the result from these processes can be used to better understand the brain processes and to better recognize brain patterns, and thus to extract new knowledge that may reside within the SSTD. From the experimental results we can conclude that the NeuCube architecture is capable of producing significantly more accurate classification results when compared with standard machine learning methods such as SVM and MLP. Moreover, the NeuCube method facilitates deep learning of the SSTD and deeper analysis of the spatio-temporal characteristics and patterns in the fMRI SSTD.

Keywords

spatio- spectro- temporal data functional Magnetic Resonance Imaging (fMRI) evolving spiking neural networks NeuCube deep learning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Norhanifah Murli
    • 1
    • 2
  • Nikola Kasabov
    • 1
  • Bana Handaga
    • 3
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  2. 2.UniversitiTun Hussein Onn MalaysiaJohorMalaysia
  3. 3.Universitas Muhammadiyah SurakartaIndonesia

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