NeuCube Neuromorphic Framework for Spatio-temporal Brain Data and Its Python Implementation

  • Nathan Scott
  • Nikola Kasabov
  • Giacomo Indiveri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)


Classification and knowledge extraction from complex spatio-temporal brain data such as EEG or fMRI is a complex challenge. A novel architecture named the NeuCube has been established in prior literature to address this. A number of key points in the implementation of this framework, including modular design, extensibility, scalability, the source of the biologically inspired spatial structure, encoding, classification, and visualisation tools must be considered. A Python version of this framework that conforms to these guidelines has been implemented.


NeuCube Neurogenetic Neuromorphic Neuroinformatic Spiking Neural Network Pattern Recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nathan Scott
    • 1
  • Nikola Kasabov
    • 1
  • Giacomo Indiveri
    • 2
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyNew Zealand
  2. 2.Neuromophic Cognitive Systems, Institute of NeuroinformaticsUniversity of Zurich and ETH ZurichSwitzerland

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