Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7311)

Abstract

Spatio- and spectro-temporal data (SSTD) are 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 organisation and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. The 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 are potentially capable of modelling complex information processes due to their 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-organising manner. The paper reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN) and computational neuro-genetic 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.

Keywords

spatio-temporal data spectro-temporal data pattern recognition spiking neural networks gene regulatory networks computational neurogenetic modeling probabilistic modeling personalized modeling EEG data 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Acharya, R., Chua, E.C.P., Chua, K.C., Min, L.C., Tamura, T.: Analysis and Automatic Identification of Sleep Stages using Higher Order Spectra. Int. Journal of Neural Systems 20(6), 509–521 (2010)CrossRefGoogle Scholar
  2. 2.
    Arel, I., Rose, D.C., Karnowski, T.P.: Deep Machine Learning: A New Frontier in Artificial Intelligence Research. IEEE Comput. Intelligence Magazine 5(4), 13–18 (2010)CrossRefGoogle Scholar
  3. 3.
    Arel, I., Rose, D., Coop, B.: DeSTIN: A deep learning architecture with application to high-dimensional robust pattern recognition. In: Proc. 2008 AAAI Workshop Biologically Inspired Cognitive Architectures, BICA (2008)Google Scholar
  4. 4.
    Arel, I., Rose, D., Karnovski, T.: Deep Machine Learning – A New Frontier in Artificial Intelligence Research. IEEE CI Magazine, 13–18 (November 2010)Google Scholar
  5. 5.
    Barbado, M., Fablet, K., Ronjat, M., De Waard, M.: Gene regulation by voltage-dependent calcium channels. Biochimica et Biophysica Acta 1793, 1096–1104 (2009)CrossRefGoogle Scholar
  6. 6.
    Barker-Collo, S., Feigin, V.L., Parag, V., Lawes, C.M.M., Senior, H.: Auckland Stroke Outcomes Study. Neurology 75(18), 1608–1616 (2010)CrossRefGoogle Scholar
  7. 7.
    Belatreche, A., Maguire, L.P., McGinnity, M.: Advances in Design and Application of Spiking Neural Networks. Soft Comput. 11(3), 239–248 (2006)MATHCrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Bengio, Y.: Learning Deep Architectures for AI. Found. Trends. Mach. Learning 2(1), 1–127 (2009)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Benuskova, L., Kasabov, N.: Computational neuro-genetic modelling, 290 pages. Springer, New York (2007)Google Scholar
  11. 11.
    Berry, M.J., Warland, D.K., Meister, M.: The structure and precision of retinal spiketrains. PNAS 94(10), 5411–5416 (1997)CrossRefGoogle Scholar
  12. 12.
    Bohte, S., Kok, J., LaPoutre, J.: Applications of spiking neural networks. Information Processing Letters 95(6), 519–520 (2005)MATHCrossRefGoogle Scholar
  13. 13.
    Bohte, S.M.: The evidence for neural information processing with precise spike-times: A survey. Natural Computing 3 (2004)Google Scholar
  14. 14.
    Brader, J., Senn, W., Fusi, S.: Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Computation 19(11), 2881–2912 (2007)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Brader, J.M., Senn, W., Fusi, S.: Learning Real-World Stimuli in a Neural Net-work with Spike-Driven Synaptic Dynamics. Neural Comput. 19(11), 2881–2912 (2007)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris, F.C., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A.P., Boustani, S.E., Destexhe, A.: Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349–398 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Buonomano, D., Maass, W.: State-dependent computations: Spatio-temporal processing in cortical networks. Nature Reviews, Neuroscience 10, 113–125 (2009)CrossRefGoogle Scholar
  18. 18.
    De Zeeuw, C.I., Hoebeek, F.E., Bosman, L.W.J., Schonewille, M.: Spati-otemporal firing patterns in the cerebellum. Nature Reviews Neurosc. 12, 327–344 (2011)CrossRefGoogle Scholar
  19. 19.
    Shortall, C.R., Moore, A., Smith, E., Hall, M.J., Woiwod, I.P., Harrington, R.: Long-term changes in the abundance of flying insects. Insect Conservation and Diversity 2(4), 251–260 (2009)CrossRefGoogle Scholar
  20. 20.
    Cowburn, P.J., Cleland, J.G.F., Coats, A.J.S., Komajda, M.: Risk stratifica-tion in chronic heart failure. Eur. Heart J. 19, 696–710 (1996)CrossRefGoogle Scholar
  21. 21.
    Craig, D.A., Nguyen, H.T.: Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control, Engin. In: Medicine and Biology Society, EMBS 2007, pp. 2544–2547 (2007)Google Scholar
  22. 22.
    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
  23. 23.
    Delbruck, T.: jAER open source project (2007), http://jaer.wiki.sourceforge.net
  24. 24.
    Douglas, R., Mahowald, M.: Silicon Neurons. In: Arbib, M. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 282–289. MIT Press (1995)Google Scholar
  25. 25.
    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. IEEE (June 2012)Google Scholar
  26. 26.
    Ferreira, A., Almeida, C., Georgieva, P., Tomé, A., Silva, F.: Advances in EEG-based Biometry. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 287–295. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  27. 27.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar
  28. 28.
    Florian, R.V.: The chronotron: a neuron that learns to fire temporally-precise spike patterns (2010)Google Scholar
  29. 29.
    Furber, S., Temple, S.: Neural systems engineering, Interface. J.of the Royal Society 4, 193–206 (2007)Google Scholar
  30. 30.
    Fusi, S., Annunziato, M., Badoni, D., Salamon, A., Amit, D.: Spike-driven synaptic plasticity: theory, simulation, VLSI implementation. Neural Computation 12(10), 2227–2258 (2000)CrossRefGoogle Scholar
  31. 31.
    Gene and Disease (2005), NCBI, http://www.ncbi.nlm.nih.gov
  32. 32.
    Gerstner, W.: Time structure of the activity of neural network models. Phys. Rev 51, 738–758 (1995)Google Scholar
  33. 33.
    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 Publs. (2001)Google Scholar
  34. 34.
    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
  35. 35.
    Ghosh-Dastidar, S., Adeli, H.: A New Supervised Learning Algorithm for Multiple Spiking Neural Networks with Application in Epilepsy and Seizure Detection. Neural Networks 22(10), 1419–1431 (2009)CrossRefGoogle Scholar
  36. 36.
    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
  37. 37.
    Goodman, E., Ventura, D.: Spatiotemporal pattern recognition via liquid state ma-chines. In: International Joint Conference on Neural Networks, IJCNN 2006, Vancouver, BC, pp. 3848–3853 (2006)Google Scholar
  38. 38.
    Gutig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)CrossRefGoogle Scholar
  39. 39.
    Hebb, D.: The Organization of Behavior. John Wiley and Sons, New York (1949)Google Scholar
  40. 40.
    Henley, J.M., Barker, E.A., Glebov, O.O.: Routes, destinations and delays: recent ad-vances in AMPA receptor trafficking. Trends in Neurosc. 34(5), 258–268 (2011)CrossRefGoogle Scholar
  41. 41.
    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)CrossRefGoogle Scholar
  42. 42.
    Hopfield, J.: Pattern recognition computation using action potential timing for stimulus representation. Nature 376, 33–36 (1995)CrossRefGoogle Scholar
  43. 43.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. PNAS USA 79, 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Hugo, G.E., Ines, S.: Time and category information in pattern-based codes. Frontiers in Computational Neuroscience 4(0) (2010)Google Scholar
  45. 45.
    Huguenard, J.R.: Reliability of axonal propagation: The spike doesn’t stop here. PNAS 97(17), 9349–9350 (2000)CrossRefGoogle Scholar
  46. 46.
    Iglesias, J., Villa, A.E.P.: Emergence of Preferred Firing Sequences in Large Spiking Neural Networks During Simulated Neuronal Development. Int. Journal of Neural Systems 18(4), 267–277 (2008)CrossRefGoogle Scholar
  47. 47.
    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
  48. 48.
    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
  49. 49.
    Indiveri, G., Stefanini, F., Chicca, E.: Spike-based learning with a generalized inte-grate 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
  50. 50.
    Indiviery, G., Horiuchi, T.: Frontiers in Neuromorphic Engineering. Frontiers in Neuroscience 5, 118 (2011)Google Scholar
  51. 51.
    Isa, T., Fetz, E.E., Muller, K.: Recent advances in brain-machine interfaces. Neural Networks, Brain-Machine Interface 22(9), 1201–1202 (2009)Google Scholar
  52. 52.
    Izhikevich, E.: Simple model of spiking neurons. IEEE Trans. on Neural Networks 14(6), 1569–1572 (2003)MathSciNetCrossRefGoogle Scholar
  53. 53.
    Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE TNN 15(5), 1063–1070 (2004)Google Scholar
  54. 54.
    Izhikevich, E.M., Edelman, G.M.: Large-Scale Model of Mammalian Thalamocortical Systems. PNAS 105, 3593–3598 (2008)CrossRefGoogle Scholar
  55. 55.
    Izhikevich, E.: Polychronization: Computation with Spikes. Neural Computation 18, 245–282 (2006)MathSciNetMATHCrossRefGoogle Scholar
  56. 56.
    Johnston, S.P., Prasad, G., Maguire, L., McGinnity, T.M.: FPGA Hard-ware/software co-design methodology - towards evolvable spiking networks for robotics application. Int. J. Neural Systems 20(6), 447–461 (2010)CrossRefGoogle Scholar
  57. 57.
    Kasabov, N.: Foundations of Neural Networks. In: Fuzzy Systems and Knowledge Engineering, p. 550. MIT Press, Cambridge (1996)Google Scholar
  58. 58.
    Kasabov, N., Hu, Y.: Integrated optimisation method for personalised modelling and case study applications. Int. Journal of Functional Informatics and Personalised Medicine 3(3), 236–256 (2010)CrossRefGoogle Scholar
  59. 59.
    Kasabov, N.: Data Analysis and Predictive Systems and Related Methodologies – Person-alised Trait Modelling System. PCT/NZ2009/000222, NZ PatentGoogle Scholar
  60. 60.
    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
  61. 61.
    Kasabov, N., Evolving connectionist systems: The knowledge engineering approach. Springer (2003, 2007)Google Scholar
  62. 62.
    Kasabov, N.: Global, local and personalised modelling and profile discovery in Bioinformatics: An integrated approach. Pattern Recogn. Letters 28(6), 673–685 (2007)CrossRefGoogle Scholar
  63. 63.
    Kasabov, N., Benuskova, L., Wysoski, S.: A Computational Neurogenetic Model of a Spiking Neuron. In: Proc. IJCNN 2005 Conf., vol. 1, pp. 446–451. IEEE Press (2005)Google Scholar
  64. 64.
    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
  65. 65.
    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 (2011)Google Scholar
  66. 66.
    Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neural Netw. 23(1), 16–19 (2010)CrossRefGoogle Scholar
  67. 67.
    Kilpatrick, Z.P., Bresloff, P.C.: Effect of synaptic depression and adaptation on spatio-temporal dynamics of an excitatory neural networks. Physica D 239, 547–560 (2010)MathSciNetCrossRefGoogle Scholar
  68. 68.
    Kistler, G., Gerstner, W.: Spiking Neuron Models - Single Neurons, Populations, Plasticity. Cambridge Univ. Press (2002)Google Scholar
  69. 69.
    Legenstein, R., Naeger, C., Maass, W.: What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? Neural Computation 17(11), 2337–2382 (2005)MathSciNetMATHCrossRefGoogle Scholar
  70. 70.
    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)Google Scholar
  71. 71.
    Maass, W., Markram, H.: Synapses as dynamic memory buffers. Neural Network 15(2), 155–161 (2002)CrossRefGoogle Scholar
  72. 72.
    Maass, W., Zador, A.M.: Computing and learning with dynamic synapses. In: Pulsed Neural Networks, pp. 321–336. MIT Press (1999)Google Scholar
  73. 73.
    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)MATHCrossRefGoogle Scholar
  74. 74.
    Meng, Y., Y. Jin, J. Yin, and M. Conforth (2010) Human activity detection using spiking neural networks regulated by a gene regulatory network. Proc. Int. Joint Conf. on Neural Net-works (IJCNN), IEEE Press, pp.2232-2237, Barcelona, July 2010. Google Scholar
  75. 75.
    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
  76. 76.
    Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: Evolving Spike Pattern Association Neurons and Neural Networks. Neurocomputing (in print)Google Scholar
  77. 77.
    Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: SPAN: Spike Pattern Association Neuron for Learning Spatio-Temporal Sequences. International Journal of Neural Systems (in print, 2012)Google Scholar
  78. 78.
    Natschläger, T., Maass, W.: Spiking neurons and the induction of finite state machines. Theoretical Computer Science - Natural Computing 287(1), 251–265 (2002)MathSciNetMATHCrossRefGoogle Scholar
  79. 79.
    NeMo spiking neural network simulator, http://www.doc.ic.ac.uk/~akf/nemo/index.html
  80. 80.
    Nichols, E., McDaid, L.J., Siddique, N.H.: Case Study on Self-organizing Spiking Neural Networks for Robot Navigation. International Journal of Neural Systems 20(6), 501–508 (2010)CrossRefGoogle Scholar
  81. 81.
    Norton, D., Ventura, D.: Improving liquid state machines through iterative refinement of the reservoir. Neurocomputing 73, 2893–2904 (2010)CrossRefGoogle Scholar
  82. 82.
    Nuzlu, H., Kasabov, N., Shamsuddin, S., Widiputra, H., Dhoble: An Extended Evolving Spiking Neural Network Model for Spatio-Temporal Pattern Classification. In: Proc. IJCNN, California, USA, pp. 2653–2656. IEEE Press (2011)Google Scholar
  83. 83.
    Nuzly, H., Kasabov, N., Shamsuddin, S.: Probabilistic Evolving Spiking Neural Network Optimization Using Dynamic Quantum Inspired Particle Swarm Optimization. In: ICONIP 2010, Part I. LNCS, vol. 6443 (2010)Google Scholar
  84. 84.
    Ozawa, S., Pang, S., Kasabov, N.: Incremental Learning of Chunk Data for On-line Pattern Classification Systems. IEEE Trans. Neural Networks 19(6), 1061–1074 (2008)CrossRefGoogle Scholar
  85. 85.
    Pang, S., Ozawa, S., Kasabov, N.: Incremental Linear Discriminant Analysis for Classification of Data Streams. IEEE Trans. SMC-B 35(5), 905–914 (2005)Google Scholar
  86. 86.
    Pfurtscheller, G., Leeb, R., Keinrath, C., Friedman, D., Neuper, C., Guger, C., Slater, M.: Walking from thought. Brain Research 1071(1), 145–152 (2006)CrossRefGoogle Scholar
  87. 87.
    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)MathSciNetMATHCrossRefGoogle Scholar
  88. 88.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–285 (1989)CrossRefGoogle Scholar
  89. 89.
    Rast, A.D., Jin, X., Galluppi, F., Plana, L.A., Patterson, C., Furber, S.: Scalable Event-Driven Native Parallel Processing: The SpiNNaker Neuromimetic System. In: Proc. of the ACM International Conference on Computing Frontiers, Bertinoro, Italy, May 17-19, pp. 21–29 (2010) ISBN 978-1-4503-0044-5Google Scholar
  90. 90.
    Reinagel, P., Reid, R.C.: Precise firing events are conserved across neurons. Journal of Neuroscience 22(16), 6837–6841 (2002)Google Scholar
  91. 91.
    Reinagel, R., Reid, R.C.: Temporal coding of visual information in the thalamus. Journal of Neuroscience 20(14), 5392–5400 (2000)Google Scholar
  92. 92.
    Riesenhuber, M., Poggio, T.: Hierarchical Model of Object Recognition in Cortex. Nature Neuroscience 2, 1019–1025 (1999)CrossRefGoogle Scholar
  93. 93.
    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
  94. 94.
    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
  95. 95.
    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
  96. 96.
    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
  97. 97.
    Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated Feature and Parameter Optimization for Evolving Spiking Neural Netw.: Exploring Heterogeneous Probabilistic Model. Neural Netw. 22, 623–632 (2009)CrossRefGoogle Scholar
  98. 98.
    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
  99. 99.
    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
  100. 100.
    Schrauwen, B., Van Campenhout, J.: BSA, a fast and accurate spike train encoding scheme. In: Proceedings of the International Joint Conference on Neural Networks, vol. 4, pp. 2825–2830. IEEE (2003)Google Scholar
  101. 101.
    Soltic, S., Kasabov, N.: Knowledge extraction from evolving spiking neural networks with rank order population coding. International Journal of Neural Systems 20(6), 437–445 (2010)CrossRefGoogle Scholar
  102. 102.
    Sona, D., Veeramachaneni, H., Olivetti, E., Avesani, P.: Inferring cognition from fMRI brain images. In: Proc. of IJCNN. IEEE Press (2011)Google Scholar
  103. 103.
    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
  104. 104.
    Theunissen, F., Miller, J.P.: Temporal encoding in nervous systems: a rigorous definition. Journal of Computational Neuroscience 2(2), 149–162 (1995)CrossRefGoogle Scholar
  105. 105.
    Thorpe, S., Gautrais, J.: Rank order coding. Computational Neuroscience: Trends in Research 13, 113–119 (1998)Google Scholar
  106. 106.
    Thorpe, S., Delorme, A., et al.: Spike-based strategies for rapid processing. Neural Netw. 14(6-7), 715–725 (2001)CrossRefGoogle Scholar
  107. 107.
    van Schaik, A., Shih-Chii Liu, L.: AER EAR: a matched silicon cochlea pair with address event representation interface. In: Proc. of ISCAS - IEEE Int. Symp. Circuits and Systems, May 23-26, vol. 5, pp. 4213–4216 (2005)Google Scholar
  108. 108.
    Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20(3), 391–403 (2007)MATHCrossRefGoogle Scholar
  109. 109.
    Villa, A.E.P., et al.: Cross-channel coupling of neuronal activity in parvalbumin-deficient mice susceptible to epileptic seizures. Epilepsia 46(suppl. 6), 359 (2005)Google Scholar
  110. 110.
    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
  111. 111.
    Watts, M.: A Decade of Kasabov’s Evolving Connectionist Systems: A Review. IEEE Trans. Systems, Man and Cybernetics- Part C: Appl. and Reviews 39(3), 253–269 (2009)MathSciNetCrossRefGoogle Scholar
  112. 112.
    Weston, I., Ratle, F., Collobert, R.: Deep learning via semi-supervised embedding. In: Proc. 25th Int. Conf. Machine Learning, pp. 1168–1175 (2008)Google Scholar
  113. 113.
    Widiputra, H., Pears, R., Kasabov, N.: Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 161–172. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  114. 114.
    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
  115. 115.
    Wysoski, S., Benuskova, L., Kasabov, N.: Evolving spiking neural networks for audiovisual information processing. Neural Networks 23(7), 819–835 (2010)CrossRefGoogle Scholar
  116. 116.
    Jin, X., Lujan, M., Plana, L.A., Davies, S., Temple, S., Furber, S.: Modelling Spiking Neural Networks on SpiNNaker. Computing in Science & Engineering 12(5), 91–97 (2010) ISSN 1521-961 CrossRefGoogle Scholar
  117. 117.
    Yu, Y.C., et al.: Specific synapses develop preferentially among sister excitatory neurons in the neocortex. Nature 458, 501–504 (2009)CrossRefGoogle Scholar
  118. 118.
    Zhdanov, V.P.: Kinetic models of gene expression including non-coding RNAs. Phys. Reports 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

Personalised recommendations