Brain-like Information Processing for Spatio-Temporal Pattern Recognition

  • Nikola Kasabov


Information processes in the brain, such as gene and protein expression, learning, memory, perception, cognition, consciousness are all spatio- and/or spectro temporal. Modelling such processes would require sophisticated information science methods and the best ones could be the brain-inspired ones, that use the same brain information processing principles. Spatio and spectro-temporal data (SSTD) are also the most common types of data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, there is lack of methods for the efficient analysis of such data and for spatio-temporal pattern recognition (STPR). The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Its organization and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. Brain-inspired spiking neural networks (SNN) are considered the third generation of neural networks and are a promising paradigm for the creation of new intelligent ICT for SSTD. This new generation of computational models and systems is potentially capable of modeling complex information processes due to the ability to represent and integrate different information dimensions, such as time, space, frequency, and phase, and to deal with large volumes of data in an adaptive and self-organizing manner. This chapter reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN), and computational neurogenetic models (CNGM). Software and hardware implementations and some pilot applications for audio-visual pattern recognition, EEG data-analysis, cognitive robotic systems, BCI, neurodegenerative diseases, and others are discussed.


Gene Regulatory Network Spike Activity Synaptic Weight Synaptic Efficacy Spike Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



address event representation


α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid


(amino-methylisoxazole-propionic acid) receptor


artificial neural network


brain-computer interface


chloride channel


computational neurogenetic model




field-programmable gate array


GABAA receptor


GABAB receptor


GABAA receptor


GABAB receptor


guanosine diphosphate


gene regulatory network


hidden Markov model


individual-based model


integrate-and-fire model


kalium (potassium) voltage-gated channel


linear discriminant analysis


leaky integrate-and-fire neuron


leaky IFM


liquid state machine


long-term depression


long-term potentiation


multilayer perceptron




(N-methyl-d-aspartate acid) NMDA receptor


principle component analysis


particle swarm optimization


post-synaptic potential


remote supervised method


sodium voltage-gated channel


spike driven synaptic plasticity


spiking neural network


spike response model


spatio and spectro-temporal data


spike-timing dependent plasticity


spatio-temporal pattern recognition


evolving spiking neural network


functional magnetic resonance imaging


messenger RNA


  1. 47.1.
  2. 47.2.
    The FMRIB Centre, University of Oxford,
  3. 47.3.
    D.A. Craig, H.T. Nguyen: Adaptive EEG thought pattern classifier for advanced wheelchair control, Proc. Eng. Med. Biol. Soc. – EMBSʼ07 (2007) pp. 2544–2547Google Scholar
  4. 47.4.
    A. Ferreira, C. Almeida, P. Georgieva, A. Tomé, F. Silva: Advances in EEG-based biometry, LNCS 6112, 287–295 (2010)Google Scholar
  5. 47.5.
    T. Isa, E.E. Fetz, K. Müller: Recent advances in brain-machine interfaces, Neural Netw. 22(9), 1201–1202 (2009)CrossRefGoogle Scholar
  6. 47.6.
    F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, B. Arnaldi: A review of classification algorithms for EEG-based brain–computer interfaces, J. Neural Eng. 4(2), R1–R15 (2007)CrossRefGoogle Scholar
  7. 47.7.
    S. Schliebs, N. Nuntalid, N. Kasabov: Towards spatio-temporal pattern recognition using evolving spiking neural networks, LNCS 6443, 163–170 (2010)Google Scholar
  8. 47.8.
    B. Schrauwen, J. Van Campenhout: BSA, a fast and accurate spike train encoding scheme, Neural Netw. 2003, Proc. Int. Jt. Conf., Vol. 4 (IEEE 2003) pp. 2825–2830Google Scholar
  9. 47.9.
    D. Sona, H. Veeramachaneni, E. Olivetti, P. Avesani: Inferring cognition from fMRI brain images, LNCS 4669, 869–878 (2007)Google Scholar
  10. 47.10.
    T. Delbruck: JAER open source project (2007)
  11. 47.11.
    K. Dhoble, N. Nuntalid, G. Indivery, N. Kasabov: Online spatio-temporal pattern recognition with evolving spiking neural networks utilising address event representation, rank order, and temporal spike learning, Int. Joint Conf. Neural Netw. (IJCNN) (IEEE 2012)Google Scholar
  12. 47.12.
    N. Kasabov, K. Dhoble, N. Nuntalid, A. Mohemmed: Evolving probabilistic spiking neural networks for spatio-temporal pattern recognition: A preliminary study on moving object recognition, 7064, 230–239 (2011)Google Scholar
  13. 47.13.
    A. Rokem, S. Watzl, T. Gollisch, M. Stemmler, A.V.M. Herz, I. Samengo: Spike-timing precision underlies the coding efficiency of auditory receptor neurons, J. Neurophys. 95(4), 2541–2552 (2005)CrossRefGoogle Scholar
  14. 47.14.
    A. van Schaik, L. Shih-Chii: AER EAR: A matched address event representation interface, Proc. ISCAS – IEEE Int. Symp. Circuits Syst., Vol. 5 (2005) pp. 4213–4216Google Scholar
  15. 47.15.
    P.J. Cowburn, J.G.F. Cleland, A.J.S. Coats, M. Komajda: Risk stratification in chronic heart failure, Eur. Heart J. 19, 696–710 (1996)CrossRefGoogle Scholar
  16. 47.16.
    S. Barker-Collo, V.L. Feigin, V. Parag, C.M.M. Lawes, H. Senior: Auckland stroke outcomes study, Neurology 75(18), 1608–1616 (2010)CrossRefGoogle Scholar
  17. 47.17.
    N. Kasabov: Global, local and personalised modelling and profile discovery in Bioinformatics: An integrated approach, Pattern Recogn. Lett. 28(6), 673–685 (2007)CrossRefGoogle Scholar
  18. 47.18.
    R. Schliebs: Basal forebrain cholinergic dysfunction in Alzheimerʼs disease – interrelationship with β-amyloid, inflammation and neurotrophin signaling, Neurochem. Res. 30, 895–908 (2005)CrossRefGoogle Scholar
  19. 47.19.
    N. Kasabov, R. Schliebs, H. Kojima: Probabilistic computational neurogenetic framework: From modelling cognitive systems to Alzheimerʼs disease, IEEE Trans. Auton. Ment. Dev. 3(4), 1–12 (2011)CrossRefGoogle Scholar
  20. 47.20.
    C.R. Shortall, A. Moore, E. Smith, M.J. Hall, I.P. Woiwod, R. Harrington: Long-term changes in the abundance of flying insects, Insect Conserv. Divers. 2(4), 251–260 (2009)CrossRefGoogle Scholar
  21. 47.21.
    S. Schliebs, M. Defoin-Platel, S. Worner, N. Kasabov: Integrated feature and parameter optimization for evolving spiking neural network: Exploring heterogeneous probabilistic models, Neural Netw. 22, 623–632 (2009)CrossRefGoogle Scholar
  22. 47.22.
    L.R. Rabiner: A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of IEEE 77(2), 257–285 (1989)CrossRefGoogle Scholar
  23. 47.23.
    N. Kasabov: Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (MIT Press, Cambridge 1996) p. 550zbMATHGoogle Scholar
  24. 47.24.
    I. Arel, D.C. Rose, T.P. Karnowski: Deep machine learning: A new frontier artificial intelligence research, Comput. Intell. Mag. 5(4), 13–18 (2010)CrossRefGoogle Scholar
  25. 47.25.
    I. Arel, D. Rose, B. Coop: DeSTIN: A deep learning architecture with application to high-dimensional robust pattern, Proc. 2008 AAAI Workshop Biologically Inspired Inspired Cognitive Architectures (BICA) (2008)Google Scholar
  26. 47.26.
    Y. Bengio: Learning deep architectures for AI, Found. Trends. Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 47.27.
    I. Weston, F. Ratle, R. Collobert: Deep learning via semi-supervised embedding, Proc. 25th Int. Conf. Mach. Learn. (2008) pp. 1168–1175Google Scholar
  28. 47.28.
    W. Gerstner: Time structure of the activity of neural network models, Phys. Rev. 51, 738–758 (1995)Google Scholar
  29. 47.29.
    W. Gerstner: Whatʼs different with spiking neurons?. In: Plausible Neural Networks for Biological Modelling, ed. by H. Mastebroek, H. Vos (Kluwer, Dordrecht 2001) pp. 23–48CrossRefGoogle Scholar
  30. 47.30.
    G. Kistler, W. Gerstner: Spiking neuron models – single neurons. In: Populations, Plasticity (Cambridge Univ. Press, Cambridge 2002)Google Scholar
  31. 47.31.
    S. Song, K. Miller, L. Abbott: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity, Nat. Neurosci. 3, 919–926 (2000)CrossRefGoogle Scholar
  32. 47.32.
    S. Fusi, M. Annunziato, D. Badoni, A. Salamon, D. Amit: Spike-driven synaptic plasticity: Theory, simulation, VLSI implementation, Neural Comput. 12(10), 2227–2258 (2000)CrossRefGoogle Scholar
  33. 47.33.
    A. Belatreche, L.P. Maguire, M. McGinnity: Advances in design and application of spiking neural networks, Soft Comput. 11(3), 239–248 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 47.34.
    F. Bellas, R.J. Duro, A. Faiña, D. Souto: Multilevel Darwinisb Brain (MDB): Artificial evolution in a cognitive architecture for real robots, IEEE Trans. Auton. Ment. Dev. 2, 340–354 (2010)CrossRefGoogle Scholar
  35. 47.35.
    S. Bohte, J. Kok, J. LaPoutre: Applications of spiking neural networks, Inf. Proc. Lett. 95(6), 519–520 (2005)CrossRefzbMATHGoogle Scholar
  36. 47.36.
    W. Maass, T. Natschlaeger, H. Markram: Real-time computing without stable states: A new framework for neural computation based on perturbations, Neural Comput. 14(11), 2531–2560 (2002)CrossRefzbMATHGoogle Scholar
  37. 47.37.
    N. Kasabov: Evolving Connectionist Systems: The Knowledge Engineering Approach (Springer, London 2007)zbMATHGoogle Scholar
  38. 47.38.
    S. Wysoski, L. Benuskova, N. Kasabov: Evolving spiking neural networks for audiovisual information processing, Neural Netw. 23(7), 819–835 (2010)CrossRefGoogle Scholar
  39. 47.39.
    M. Riesenhuber, T. Poggio: Hierarchical model of object recognition in cortex, Nat. Neurosci. 2, 1019–1025 (1999)CrossRefGoogle Scholar
  40. 47.40.
    L. Benuskova, N. Kasabov: Computational Neuro-Genetic Modelling (Springer, New York 2007) p. 290CrossRefGoogle Scholar
  41. 47.41.
    N. Kasabov, L. Benuskova, S. Wysoski: A computational neurogenetic model of a spiking neuron, IJCNN 2005 Conf. Proc., Vol. 1 (IEEE 2005) pp. 446–451Google Scholar
  42. 47.42.
    N. Kasabov: To spike or not to spike: A probabilistic spiking neuron model, Neural Netw. 23(1), 16–19 (2010)CrossRefGoogle Scholar
  43. 47.43.
    W. Maass, H. Markram: Synapses as dynamic memory buffers, Neural Netw. 15(2), 155–161 (2002)CrossRefGoogle Scholar
  44. 47.44.
    S. Schliebs, N. Kasabov, M. Defoin-Platel: On the probabilistic optimization of spiking neural networks, Int. J. Neural Syst. 20(6), 481–500 (2010)CrossRefGoogle Scholar
  45. 47.45.
    D. Verstraeten, B. Schrauwen, M. DʼHaene, D. Stroobandt: An experimental unification of reservoir computing methods, Neural Netw. 20(3), 391–403 (2007)CrossRefzbMATHGoogle Scholar
  46. 47.46.
    N. Kasabov, Y. Hu: Integrated optimisation method for personalised modelling and case study applications, Int. J. Funct. Inf. Personal. Med. 3(3), 236–256 (2010)Google Scholar
  47. 47.47.
    N. Kasabov: Data analysis and predictive systems and related methodologies – personalised trait modelling system, NZ Patent PCT/NZ2009/000222 (2009)Google Scholar
  48. 47.48.
    A.L. Hodgkin, A.F. Huxley: A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiol. 117, 500–544 (1952)CrossRefGoogle Scholar
  49. 47.49.
    E. Izhikevich: Simple model of spiking neurons, IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)MathSciNetCrossRefGoogle Scholar
  50. 47.50.
    E.M. Izhikevich: Which model to use for cortical spiking neurons?, Neural Netw. 15(5), 1063–1070 (2004)CrossRefGoogle Scholar
  51. 47.51.
    E.M. Izhikevich, G.M. Edelman: large-scale model of mammalian thalamocortical systems, Proc. Natl. Acad. Sci. USA 105, 3593–3598 (2008)CrossRefGoogle Scholar
  52. 47.52.
    E. Izhikevich: Polychronization: Computation with spikes, Neural Comput. 18, 245–282 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  53. 47.53.
    Z.P. Kilpatrick, P.C. Bresloff: Effect of synaptic depression and adaptation on spatio-temporal dynamics of an excitatory neural networks, Physica D 239, 547–560 (2010)MathSciNetCrossRefGoogle Scholar
  54. 47.54.
    W. Maass, A.M. Zador: Computing and learning with dynamic synapses. In: Pulsed Neural Networks (MIT Press, Cambridge 1999) pp. 321–336Google Scholar
  55. 47.55.
    J.R. Huguenard: Reliability of axonal propagation: The spike doesnʼt stop here, Proc. Natl. Acad. Sci USA 97(17), 9349–9350 (2000)CrossRefGoogle Scholar
  56. 47.56.
    S. Schliebs, A. Mohemmed, N. Kasabov: Are probabilistic spiking neural networks suitable for reservoir computing?, Int. Jt. Conf. Neural Netw. (IJCNN) (IEEE 2011) pp. 3156–3163Google Scholar
  57. 47.57.
    H. Nuzly, A. Hamed, N. Kasabov, S. Shamsuddin: Probabilistic evolving spiking neural network optimization using dynamic quantum inspired particle swarm optimization, Aust. J. Intell. Inf. Process. Syst. 11(1), 1074 (2010), available online at Google Scholar
  58. 47.58.
    S.J. Thorpe: Spike-based image processing: Can we reproduce biological vision in hardware, LNCS 7583, 516–521 (2012)Google Scholar
  59. 47.59.
    W. Gerstner, A.K. Kreiter, H. Markram, A.V.M. Herz: Neural codes: Firing rates and beyond, Proc. Natl. Acad. Sci. USA 94(24), 12740–12741 (1997)CrossRefGoogle Scholar
  60. 47.60.
    J.J. Hopfield: Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  61. 47.61.
    S.M. Bohte: The evidence for neural information processing with precise spike-times: A survey, Nat. Comput. 3(2), 195–206 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  62. 47.62.
    J. Hopfield: Pattern recognition computation using action potential timing for stimulus representation, Nature 376, 33–36 (1995)CrossRefGoogle Scholar
  63. 47.63.
    H.G. Eyherabide, I. Samengo: Time and category information in pattern-based codes, Front. Comput. Neurosci. 4, 145 (2010)CrossRefGoogle Scholar
  64. 47.64.
    F. Theunissen, J.P. Miller: Temporal encoding in nervous rigorous definition, J. Comput. Neurosci. 2(2), 149–162 (1995)CrossRefGoogle Scholar
  65. 47.65.
    S. Thorpe, A. Delorme, R. VanRullen: Spike-based strategies for rapid processing, Neural Netw. 14(6–7), 715–725 (2001)CrossRefGoogle Scholar
  66. 47.66.
    S. Thorpe, J. Gautrais: Rank order coding, Comput. Neurosci. 13, 113–119 (1998)CrossRefGoogle Scholar
  67. 47.67.
    M.J. Berry, D.K. Warland, M. Meister: The structure and precision of retinal spiketrains, Proc. Natl. Acad. Sci. USA 94(10), 5411–5416 (1997)CrossRefGoogle Scholar
  68. 47.68.
    P. Reinagel, R.C. Reid: Precise firing events are conserved across neurons, J. Neurosci. 22(16), 6837–6841 (2002)Google Scholar
  69. 47.69.
    J. Brader, W. Senn, S. Fusi: Learning real-world stimuli in a neural network with spike-driven synaptic dynamics, Neural Comput. 19(11), 2881–2912 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  70. 47.70.
    R. Legenstein, C. Naeger, W. Maass: What can a neuron learn with spike-timing-dependent plasticity?, Neural Comput. 17(11), 2337–2382 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  71. 47.71.
    D. Hebb: The Organization of Behavior (Wiley, New York 1949)Google Scholar
  72. 47.72.
    G. Indiveri, F. Stefanini, E. Chicca: Spike-based learning with a generalized integrate and fire silicon neuron, IEEE Int. Symp. Circuits Syst. (ISCAS 2010) (2010) pp. 1951–1954Google Scholar
  73. 47.73.
    T. Masquelier, R. Guyonneau, S. Thorpe: Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains, PlosONE 3(1), e1377 (2008)CrossRefGoogle Scholar
  74. 47.74.
    R. Gutig, H. Sompolinsky: The tempotron: A neuron timing-based decisions, Nat. Neurosci. 9(3), 420–428 (2006)CrossRefGoogle Scholar
  75. 47.75.
    R.V. Florian: The chronotron: A neuron that learns to fire temporally-precise spike patterns, Nature Precedings (2010), available online at
  76. 47.76.
    F. Ponulak, A. Kasinski: Supervised learning in spiking neural networks with ReSuMe: Sequence learning, Neural Comput. 22(2), 467–510 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  77. 47.77.
    A. Mohemmed, S. Schliebs, S. Matsuda, N. Kasabov: Evolving spike pattern association neurons and neural networks, Neurocomputing 107, 3–10 (2013)CrossRefGoogle Scholar
  78. 47.78.
    A. Mohemmed, S. Schliebs, S. Matsuda, N. Kasabov: SPAN: Spike pattern association neuron for learning spatio-temporal sequences, Int. J. Neural Syst. 22(4), 1–16 (2012)Google Scholar
  79. 47.79.
    M. Watts: A decade of Kasabovʼs evolving connectionist systems: A Review, IEEE Trans. Syst. Man Cybern. C 39(3), 253–269 (2009)CrossRefGoogle Scholar
  80. 47.80.
    H. Nuzlu, N. Kasabov, S. Shamsuddin, H. Widiputra, K. Dhoble: An extended evolving spiking neural network model for spatio-temporal pattern classification, Proc. IJCNN (IEEE 2011) pp. 2653–2656Google Scholar
  81. 47.81.
    E. Goodman, D. Ventura: Spatiotemporal pattern recognition via liquid state machines, Int. Jt. Conf. Neural Networks (IJCNN) ʼ06 (2006) pp. 3848–3853Google Scholar
  82. 47.82.
    S. Schliebs, H.N.A. Hamed, N. Kasabov: A reservoir-based evolving spiking neural network for on-line spatio-temporal pattern learning and recognition, 18th Int. Conf. Neural Inf. Proc. ICONIP 2011 (Springer, Shanghai 2011)Google Scholar
  83. 47.83.
    D. Norton, D. Ventura: Improving liquid state machines through iterative refinement of the reservoir, Neurocomputing 73, 2893–2904 (2010)CrossRefGoogle Scholar
  84. 47.84.
    R.A. Fisher: The use of multiple measurements in taxonomic problems, Ann. Eugen. 7, 179–188 (1936)CrossRefGoogle Scholar
  85. 47.85.
    EU FP7 Marie Curie project EvoSpike (2011–2012),
  86. 47.86.
    S. Pang, S. Ozawa, N. Kasabov: Incremental linear discriminant analysis for classification of data streams, IEEE Trans. SMC-B 35(5), 905–914 (2005)Google Scholar
  87. 47.87.
    S. Ozawa, S. Pang, N. Kasabov: Incremental learning of chunk data for on-line pattern classification systems, IEEE Trans. Neural Netw. 19(6), 1061–1074 (2008)CrossRefGoogle Scholar
  88. 47.88.
    J.M. Henley, E.A. Barker, O.O. Glebov: Routes, destinations and advances in AMPA receptor trafficking, Trends Neurosci. 34(5), 258–268 (2011)CrossRefGoogle Scholar
  89. 47.89.
    Y.C. Yu, R.S. Bultje, X. Wang, S.H. Shi: Specific synapses develop preferentially among sister excitatory neurons in the neocortex, Nature 458, 501–504 (2009)CrossRefGoogle Scholar
  90. 47.90.
    V.P. Zhdanov: Kinetic models of gene expression including non-coding RNAs, Phys. Rep. 500, 1–42 (2011)CrossRefGoogle Scholar
  91. 47.91.
    BrainMap Project:
  92. 47.92.
    Allen Institute for Brain Science:
  93. 47.93.
    Gene and Disease (2005) NCBI,
  94. 47.94.
    N. Kasabov, S. Schliebs, A. Mohemmed: Modelling the effect of genes on the dynamics of probabilistic spiking neural networks for computational neurogenetic modelling, Proc. 6th Meet. Comp. Intell. Bioinfor. Biostat. (CIBB) 2011 (Springer 2011)Google Scholar
  95. 47.95.
    M. Barbado, K. Fablet, M. Ronjat, M. De Waard: Gene regulation by voltage-dependent calcium channels, Biochim. Biophys. Acta 1793, 1096–1104 (2009)CrossRefGoogle Scholar
  96. 47.96.
    A. Mohemmed, S. Matsuda, S. Schliebs, K. Dhoble, N. Kasabov: Optimization of spiking neural networks with dynamic synapses for spike sequence generation using PSO, Proc. Int. Joint Conf. Neural Netw. (IEEE, San Jose 2011) pp. 2969–2974Google Scholar
  97. 47.97.
    M. Defoin-Platel, S. Schliebs, N. Kasabov: Quantum-inspired evolutionary algorithm: A multi-model EDA, IEEE Trans. Evol. Comput. 13(6), 1218–1232 (2009)CrossRefGoogle Scholar
  98. 47.98.
    Neuromorphic Cognitive Systems Group, Institute for Neuroinformatics, ETH and University of Zurich,
  99. 47.99.
    R. Douglas, M. Mahowald: Silicon neurons. In: The Handbook of Brain Theory and Neural Networks, ed. by M. Arbib (MIT, Cambridge 1995) pp. 282–289Google Scholar
  100. 47.100.
    R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J.M. Bower, M. Diesmann, A. Morrison, P.H. Goodman, F.C. Harris, M. Zirpe, T. Natschläger, D. Pecevski, B. Ermentrout, M. Djurfeldt, A. Lansner, O. Rochel, T. Vieville, E. Muller, A.P. Davison, S.E. Boustani, A. Destexhe: Simulation of networks of spiking neurons: A review of tools and strategies, J. Comput. Neurosci. 23, 349–398 (2007)MathSciNetCrossRefGoogle Scholar
  101. 47.101.
    S. Furber, S. Temple: Neural systems engineering, Interface J. R. Soc. 4, 193–206 (2007)CrossRefGoogle Scholar
  102. 47.102.
    jAER Open Source Project:
  103. 47.103.
    NeMo spiking neural network simulator,∼akf/nemo/index.html
  104. 47.104.
    G. Indiveri, B. Linares-Barranco, T. Hamilton, A. Van Schaik, R. Etienne-Cummings, T. Delbruck, S. Liu, P. Dudek, P. Häfliger, S. Renaud: Neuromorphic silicon neuron circuits, Front. Neurosci. 5, 1–23 (2011)Google Scholar
  105. 47.105.
    G. Indiveri, E. Chicca, R.J. Douglas: Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition, Cogn. Comput. 1(2), 119–127 (2009)CrossRefGoogle Scholar
  106. 47.106.
    G. Indiviery, T. Horiuchi: Frontiers in neuromorphic engineering, Front. Neurosci. 5, 118 (2011)Google Scholar
  107. 47.107.
    A.D. Rast, X. Jin, F. Galluppi, L.A. Plana, C. Patterson, S. Furber: Scalable event-driven native parallel processing: The SpiNNaker neuromimetic system, Proc. ACM Int. Conf. Comput. Front. (ACM 2010) pp. 21–29Google Scholar
  108. 47.108.
    X. Jin, M. Lujan, L.A. Plana, S. Davies, S. Temple, S. Furber: Modelling spiking neural networks on SpiNNaker, Comput. Sci. Eng. 12(5), 91–97 (2010)CrossRefGoogle Scholar
  109. 47.109.
    S.P. Johnston, G. Prasad, L. Maguire, T.M. McGinnity: FPGA Hardware/software co-design methodology – towards evolvable spiking networks for robotics application, Int. J. Neural Syst. 20(6), 447–461 (2010)CrossRefGoogle Scholar
  110. 47.110.
  111. 47.111.
    R. Acharya, E.C.P. Chua, K.C. Chua, L.C. Min, T. Tamura: Analysis and automatic identification of sleep stages using higher order spectra, Int. J. Neural Syst. 20(6), 509–521 (2010)CrossRefGoogle Scholar
  112. 47.112.
    S. Ghosh-Dastidar, H. Adeli: A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection, Neural Netw. 22(10), 1419–1431 (2009)CrossRefGoogle Scholar
  113. 47.113.
    S. Ghosh-Dastidar, H. Adeli: Improved spiking neural networks for EEG classification and epilepsy and seizure detection, Integr. Comput.-Aided Eng. 14(3), 187–212 (2007)Google Scholar
  114. 47.114.
    A.E.P. Villa, Y. Asai, I. Tetko, B. Pardo, M.R. Celio, B. Schwaller: Cross-channel coupling of neuronal activity in parvalbumin-deficient mice susceptible to epileptic seizures, Epilepsia 46(6), 359 (2005)Google Scholar
  115. 47.115.
    G. Pfurtscheller, R. Leeb, C. Keinrath, D. Friedman, C. Neuper, C. Guger, M. Slater: Walking from thought, Brain Res. 1071(1), 145–152 (2006)CrossRefGoogle Scholar
  116. 47.116.
    E. Nichols, L.J. McDaid, N.H. Siddique: Case study on self-organizing spiking neural networks for robot navigation, Int. J. Neural Syst. 20(6), 501–508 (2010)CrossRefGoogle Scholar
  117. 47.117.
    X. Wang, Z.G. Hou, A. Zou, M. Tan, L. Cheng: A behavior controller for mobile robot based on spiking neural networks, Neurocomputing 71(4–6), 655–666 (2008)CrossRefGoogle Scholar
  118. 47.118.
    D. Buonomano, W. Maass: State-dependent computations: Spatio-temporal processing in cortical networks, Nat. Rev. Neurosci. 10, 113–125 (2009)CrossRefGoogle Scholar
  119. 47.119.
    T. Natschläger, W. Maass: Spiking neurons and the induction of finite state machines, Theor. Comput. Sci. Nat. Comput. 287(1), 251–265 (2002)CrossRefMathSciNetzbMATHGoogle Scholar
  120. 47.120.
    S. Soltic, N. Kasabov: Knowledge extraction from evolving spiking neural networks with rank order population coding, Int. J. Neural Syst. 20(6), 437–445 (2010)CrossRefGoogle Scholar
  121. 47.121.
    Y. Meng, Y. Jin, J. Yin, M. Conforth: Human activity detection using spiking neural networks regulated by a gene regulatory network, Proc. Int. Jt. Conf. Neural Netw. (IJCNN) (IEEE, Barcelona 2010) pp. 2232–2237Google Scholar
  122. 47.122.
    R. Pears, H. Widiputra, N. Kasabov: Evolving integrated multi-model framework for on-line multiple time series prediction, Evol. Syst. 4(2), 99–117 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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