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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
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)
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)
Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neur. Netw. 23(1), 16–19 (2010)
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)
Indiveri, G., Horiuchi, T.K.: Frontiers in neuromorphic engineering. Frontiers in Neuroscience 5, 1–2 (2011)
Furber, S.: To Build a Brain. IEEE Spectrum 49(8), 39–41 (2012)
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)
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)
Hawrylycz, M.J., et al.: An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Scott, N., Kasabov, N., Indiveri, G. (2013). NeuCube Neuromorphic Framework for Spatio-temporal Brain Data and Its Python Implementation. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)