NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals

  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7477)


The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Spatio- and spectro-temporal data (SSTD) are the most common data collected to measure brain signals and brain activities, along with the recently obtained gene and protein data. Yet, there are no computational models to integrate all these different types of data into a single model to help understand brain processes and for a better brain signal pattern recognition. The EU FP7 Marie Curie IIF EvoSpike project develops methods and tools for spatio and spectro temporal pattern recognition. This paper proposes a new evolving spiking model called NeuCube as part of the EvoSpike project, especially for modeling brain data. The NeuCube is 3D evolving Neurogenetic Brain Cube of spiking neurons that is an approximate map of structural and functional areas of interest of an animal or human brain. Optionally, gene information is included in the NeuCube in the form of gene regulatory networks that relate to spiking neuronal parameters of interest. Different types of brain SSTD can be used to train a NeuCube, including: EEG, fMRI, video-, image- and sound data, complex multimodal data. Potential applications are: EEG -, fMRI-, and multimodal brain data modeling and pattern recognition; Brain-Computer Interfaces; cognitive and emotional robots; neuro-prosthetics and neuro-rehabilitation; modeling brain diseases. Analysis of the internal structure of the model can trigger new hypotheses about spatio-temporal pathways in the brain.


evolving neurogenetic brain cube spatio/spectro-temporal brain data pattern recognition spiking neural networks gene regulatory networks computational neuro-genetic modelling probabilistic modeling personalized modeling EEG fMRI 


  1. 1.
    Belatreche, A., Maguire, L.P., McGinnity, M.: Advances in Design and Application of Spiking Neural Networks. Soft Comput. 11(3), 239–248 (2006)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bellas, F., Duro, R.J., Faiña, A., Souto, D.: MDB: Artificial Evolution in a Cognitive Architecture for Real Robots. IEEE Transactions on Autonomous Mental Development 2, 340–354 (2010)CrossRefGoogle Scholar
  3. 3.
    Benuskova, L., Kasabov, N.: Computational neuro-genetic modelling, 290 pages. Springer, New York (2007)CrossRefGoogle Scholar
  4. 4.
    Berry, M.J., Warland, D.K., Meister, M.: The structure and precision of retinal spiketrains. PNAS 94(10), 5411–5416 (1997)CrossRefGoogle Scholar
  5. 5.
    Bohte, S., Kok, J., LaPoutre, J.: Applications of spiking neural networks. Information Processing Letters 95(6), 519–520 (2005)zbMATHCrossRefGoogle Scholar
  6. 6.
    Bohte, S.M.: The evidence for neural information processing with precise spike-times: A survey. Natural Computing 3 (2004)MathSciNetGoogle Scholar
  7. 7.
    Brette, R., et al.: Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neuroscience 23, 349–398 (2007)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Buonomano, D., Maass, W.: State-dependent computations: Spatio-temporal processing in cortical networks. Nature Reviews, Neuroscience 10, 113–125 (2009)CrossRefGoogle Scholar
  9. 9.
    De Zeeuw, C.I., Hoebeek, F.E., Bosman, L.W.J., Schonewille, M.: Spatiotemporal firing patterns in the cerebellum. Nature Reviews Neuroscience 12, 327–344 (2011), doi:10.1038/nrn3011CrossRefGoogle Scholar
  10. 10.
    Craig, D.A., Nguyen, H.T.: Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control. In: Engineering in Medicine and Biology Society- EMBS 2007, pp. 2544–2547 (2007)Google Scholar
  11. 11.
    Defoin-Platel, M., Schliebs, S., Kasabov, N.: Quantum-inspired Evolutionary Algorithm: A multi-model EDA. IEEE Trans. Evolutionary Computation 13(6), 1218–1232 (2009)CrossRefGoogle Scholar
  12. 12.
    Delbruck, T.: jAER open source project (2007),
  13. 13.
    Dhoble, K., Nuntalid, N., Indivery, G., Kasabov, N.: Online Spatio-Temporal Pattern Recognition with Evolving Spiking Neural Networks utilising Address Event Representation, Rank Order, and Temporal Spike Learning. In: Proc. IJCNN 2012, Brisbane, pp. 554–560. IEEE Press (June 2012)Google Scholar
  14. 14.
    Doya, K., Sejnowski, T.: A Computational Model of Avian Song Learning. In: Gazzaniga, M. (ed.) The New Cognitive Neuroscience, pp. 469–482. MIT PressGoogle Scholar
  15. 15.
    Ferreira, A., Almeida, C., Georgieva, P., Tomé, A., Silva, F.: Advances in EEG-based Biometry. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6112, pp. 287–295. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Florian, R.V.: The chronotron: a neuron that learns to fire temporally-precise spike patternsGoogle Scholar
  17. 17.
    Furber, S., Temple, S.: Neural systems engineering, Interface. J. of the Royal Society 4, 193–206 (2007)Google Scholar
  18. 18.
    Gene and Disease, NCBI (2005),
  19. 19.
    Gerstner, W.: Time structure of the activity of neural network models. Phys. Rev. 51, 738–758 (1995)Google Scholar
  20. 20.
    Gerstner, W.: What’s different with spiking neurons? In: Mastebroek, H., Vos, H. (eds.) Plausible Neural Networks for Biological Modelling, pp. 23–48. Kluwer Academic Publishers (2001)Google Scholar
  21. 21.
    Gerstner, W., Kreiter, A.K., Markram, H., Herz, A.V.M.: Neural codes: firing rates and beyond. Proc. Natl. Acad. Sci. USA 94(24), 12740–12741 (1997)CrossRefGoogle Scholar
  22. 22.
    Ghosh-Dastidar, S., Adeli, H.: Improved Spiking Neural Networks for EEG Classification and Epilepsy and Seizure Detection. Integrated Computer-Aided Engineering 14(3), 187–212 (2007)Google Scholar
  23. 23.
    Gutig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)CrossRefGoogle Scholar
  24. 24.
    Hebb, D.: The Organization of Behavior. John Wiley and Sons, New York (1949)Google Scholar
  25. 25.
    Henley, J.M., Barker, E.A., Glebov, O.O.: Routes, destinations and delays: recent advances in AMPA receptor trafficking. Trends in Neuroscience 34(5), 258–268 (2011)CrossRefGoogle Scholar
  26. 26.
    Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117, 500–544 (1952)Google Scholar
  27. 27.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. PNAS USA 79, 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Indiveri, G., Linares-Barranco, B., Hamilton, T., Van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S., Dudek, P., Häfliger, P., Renaud, S., et al.: Neuromorphic silicon neuron circuits. Frontiers in Neuroscience 5 (2011)Google Scholar
  29. 29.
    Indiveri, G., Chicca, E., Douglas, R.J.: Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Computation 1(2), 119–127 (2009)CrossRefGoogle Scholar
  30. 30.
    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. (ISCAS 2010), Paris, May 30- June 02, pp. 1951–1954 (2010)Google Scholar
  31. 31.
    Isa, T., Fetz, E.E., Muller, K.: Recent advances in brain-machine interfaces. Neural Networks 22(9), 1201–1202 (2009)CrossRefGoogle Scholar
  32. 32.
    Izhikevich, E.M.: Polychronization: Computation with Spikes. Neural Computation 18, 245–282 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Izhikevich, E.M., Edelman, G.M.: Large-Scale Model of Mammalian Thalamocortical Systems. PNAS 105, 3593–3598 (2008)CrossRefGoogle Scholar
  34. 34.
    Kasabov, N., Dhoble, K., Nuntalid, N., Mohemmed, A.: Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition: A Preliminary Study on Moving Object Recognition. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part III. LNCS, vol. 7064, pp. 230–239. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  35. 35.
    Kasabov, N.: Evolving connectionist systems: The knowledge engineering approach. Springer (2007); 1st edn. (2002)Google Scholar
  36. 36.
    Kasabov, N., Benuskova, L., Wysoski, S.: A Computational Neurogenetic Model of a Spiking Neuron. In: IJCNN 2005 Conf. Proc., vol. 1, pp. 446–451. IEEE Press (2005)Google Scholar
  37. 37.
    Kasabov, N., Schliebs, R., Kojima, H.: Probabilistic Computational Neurogenetic Framework: From Modelling Cognitive Systems to Alzheimer’s Disease. IEEE Trans. Autonomous Mental Development 3(4), 1–12 (2011)CrossRefGoogle Scholar
  38. 38.
    Kasabov, N., Schliebs, S., Mohemmed, A.: Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling. In: Proc. 6th Meeting on Computational Intelligence for Bioinformatics and Biostatistics, CIBB 2011, Gargangio, Italy, June 30-July 2. LNCS (LNBI). Springer (to appear, 2012)Google Scholar
  39. 39.
    Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neural Netw. 23(1), 16–19 (2010)CrossRefGoogle Scholar
  40. 40.
    Kasabov, N.: Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition. In: Liu, J., Alippi, C., Bouchon-Meunier, B., Greenwood, G.W., Abbass, H.A. (eds.) WCCI 2012. LNCS, vol. 7311, pp. 234–260. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  41. 41.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4(2), R1–R15 (2007)CrossRefGoogle Scholar
  42. 42.
    Maass, W., Natschlaeger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)zbMATHCrossRefGoogle Scholar
  43. 43.
    Meng, Y., Jin, Y., Yin, J., Conforth, M.: Human activity detection using spiking neural networks regulated by a gene regulatory network. In: Proc. Int. Joint Conf. on Neural Networks (IJCNN), Barcelona, pp. 2232–2237. IEEE Press (July 2010)Google Scholar
  44. 44.
    Mohemmed, A., Matsuda, S., Schliebs, S., Dhoble, K., Kasabov, N.: Optimization of Spiking Neural Networks with Dynamic Synapses for Spike Sequence Generation using PSO. In: Proc. Int. Joint Conf. Neural Networks, California, USA, pp. 2969–2974. IEEE Press (2011)Google Scholar
  45. 45.
    Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: Evolving Spike Pattern Association Neurons and Neural Networks. Neurocomputing (in print)Google Scholar
  46. 46.
    Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: SPAN: Spike Pattern Association Neuron for Learning Spatio-Temporal Sequences. International Journal of Neural Systems 22(4), 1–16 (2012)CrossRefGoogle Scholar
  47. 47.
    Nuzly, H., Kasabov, N., Shamsuddin, S.: Probabilistic Evolving Spiking Neural Network Optimization Using Dynamic Quantum Inspired Particle Swarm Optimization. Australian Journal of Intelligent Information Processing Systems 11(1) (2010)Google Scholar
  48. 48.
    Nuntalid, N., Dhoble, K., Kasabov, N.: EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part I. LNCS, vol. 7062, pp. 451–460. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  49. 49.
    Ponulak, F., Kasinski, A.: Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Computation 22(2), 467–510 (2010) PMID:19842989MathSciNetzbMATHCrossRefGoogle Scholar
  50. 50.
    Reinagel, R., Reid, R.C.: Temporal coding of visual information in the thalamus. Journal of Neuroscience 20(14), 5392–5400 (2000)Google Scholar
  51. 51.
    Rokem, A., Watzl, S., Gollisch, T., Stemmler, M., Herz, A.V., Samengo, I.: Spike-timing precision underlies the coding efficiency of auditory receptor neurons. J. Neurophysiol. (2005)Google Scholar
  52. 52.
    Schliebs, R.: Basal forebrain cholinergic dysfunction in Alzheimer’s disease – interrelationship with β-amyloid, inflammation and neurotrophin signaling. Neurochemical Research 30, 895–908 (2005)CrossRefGoogle Scholar
  53. 53.
    Schliebs, S., Hamed, H.N.A., Kasabov, N.: Reservoir-Based Evolving Spiking Neural Network for Spatio-temporal Pattern Recognition. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part II. LNCS, vol. 7063, pp. 160–168. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  54. 54.
    Schliebs, S., Kasabov, N., Defoin-Platel, M.: On the Probabilistic Optimization of Spiking Neural Networks. International Journal of Neural Systems 20(6), 481–500 (2010)CrossRefGoogle Scholar
  55. 55.
    Schliebs, S., Mohemmed, A., Kasabov, N.: Are Probabilistic Spiking Neural Networks Suitable for Reservoir Computing? In: Int. Joint Conf. Neural Networks, IJCNN, San Jose, pp. 3156–3163. IEEE Press (2011)Google Scholar
  56. 56.
    Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated Feature and Parameter Optimization for Evolving Spiking Neural Netw.: Exploring Heterogeneous Probabilistic Models. Neural Netw. 22, 623–632 (2009)CrossRefGoogle Scholar
  57. 57.
    Schliebs, S., Nuntalid, N., Kasabov, N.: Towards Spatio-Temporal Pattern Recognition Using Evolving Spiking Neural Networks. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 163–170. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  58. 58.
    Sona, D., Veeramachaneni, S., Olivetti, E., Avesani, P.: Inferring Cognition from fMRI Brain Images. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 869–878. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  59. 59.
    Song, S., Miller, K., Abbott, L., et al.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 3, 919–926 (2000)CrossRefGoogle Scholar
  60. 60.
    Theunissen, F., Miller, J.P.: Temporal encoding in nervous systems: a rigorous definition. Journal of Computational Neuroscience 2(2), 149–162 (1995)CrossRefGoogle Scholar
  61. 61.
    Thorpe, S., Gautrais, J.: Rank order coding. Computational Neuroscience: Trends in Research 13, 113–119 (1998)CrossRefGoogle Scholar
  62. 62.
    Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20(3), 391–403 (2007)zbMATHCrossRefGoogle Scholar
  63. 63.
    Wang, X., Hou, Z.G., Zou, A., Tan, M., Cheng, L.: A behavior controller for mobile robot based on spiking neural networks. Neurocomputing 71(4-6), 655–666 (2008)CrossRefGoogle Scholar
  64. 64.
    Widrow, B., Lehr, M.: 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE 78(9), 1415–1442 (1990)CrossRefGoogle Scholar
  65. 65.
    Wysoski, S., Benuskova, L., Kasabov, N.: Evolving spiking neural networks for audiovisual information processing. Neural Networks 23(7), 819–835 (2010)CrossRefGoogle Scholar
  66. 66.
    Zhdanov, V.P.: Kinetic models of gene expression including non-coding RNAs. Phys. Rep. 500, 1–42 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nikola Kasabov
    • 1
    • 2
  1. 1.Knowledge Engineering and Discovery Research Institute - KEDRIAuckland University of TechnologyNew Zealand
  2. 2.Institute for Neuroinformatics - INIETH and University of ZurichSwitzerland

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