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)

Abstract

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.

Keywords

NeuCube Neurogenetic Neuromorphic Neuroinformatic Spiking Neural Network Pattern Recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kasabov, N.: NeuCube EvoSpike Architecture for Spatio-Temporal Modelling and Pattern Recognition of Brain Signals. In: Mana, N., Schwenker, F., Trentin, E. (eds.) ANNPR 2012. LNCS (LNAI), vol. 7477, pp. 225–243. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., Fox, P.T.: Automated Talairach Atlas labels for functional brain mapping. Human Brain Mapping 10, 120–131 (2000)CrossRefGoogle Scholar
  3. 3.
    Kepecs, A., van Rossum, M.C.W., Song, S., Tegner, J.: Spike-timing-dependent plasticity: common themes and divergent vistas. Biological Cybernetics 87, 446–458 (2002)CrossRefMATHGoogle Scholar
  4. 4.
    Koessler, L., Maillard, L., Benhadid, A., Vignal, J.P., Felblinger, J., Vespignani, H., Braun, M.: Automated cortical projection of EEG sensors: Anatomical correlation via the international 10-10 system. NeuroImage 46, 64–72 (2009)CrossRefGoogle Scholar
  5. 5.
    Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: SPAN: Spike Pattern Association Neuron for Learning Spatio-Temporal Sequences. Int. J. of Neural Systems 22(4), 1–16 (2012)Google Scholar
  6. 6.
    Kasabov, N., Dhoble, K., Nuntalid, N., Indiveri, G.: Dynamic Evolving Spiking Neural Networks for On-line Spatio- and Spectro-Temporal Pattern Recognition. Neural Networks 41, 188–201 (2013)CrossRefGoogle Scholar
  7. 7.
    Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neur. Netw. 23(1), 16–19 (2010)CrossRefGoogle Scholar
  8. 8.
    Indiveri, G., Stefanini, F., Chicca, E.: Spike-based learning with a generalized integrate and fire silicon neuron. In: 2010 IEEE Int. Symp. Circuits and Syst., Paris, pp. 1951–1954 (2010)Google Scholar
  9. 9.
    Indiveri, G., Horiuchi, T.K.: Frontiers in neuromorphic engineering. Frontiers in Neuroscience 5, 1–2 (2011)Google Scholar
  10. 10.
    Furber, S.: To Build a Brain. IEEE Spectrum 49(8), 39–41 (2012)CrossRefGoogle Scholar
  11. 11.
    Galluppi, F., Rast, A., Davies, S., Furber, S.: A general-purpose model translation system for a universal neural chip. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 58–65. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Serrano-Gotarredona, T., Prodromakis, T., Indiveri, G., Linares-Barranco, B., Masquelier, T.: STDP and STDP variations with memristors for spiking neuromorphic learning systems. Frontiers in Neuroscience 7 (2013)Google Scholar
  13. 13.
    Hawrylycz, M.J., et al.: An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012)CrossRefGoogle Scholar
  14. 14.
    Davison, A.P., Brüderle, D., Eppler, J.M., Kremkow, J., Muller, E., Pecevski, D.A., Perrinet, L., Yger, P.: PyNN: A common interface for neuronal network simulators. Frontiers in Neuroinformatics 2(11), 1–10 (2008)Google Scholar

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

Personalised recommendations