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Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework

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Advances in Machine Learning II

Part of the book series: Studies in Computational Intelligence ((SCI,volume 263))

Abstract

Integrative evolving connectionist systems (iECOS) integrate principles from different levels of information processing in the brain, including cognitive-, neuronal-, genetic- and quantum, in their dynamic interaction over time. The paper introduces a new framework of iECOS called integrative probabilistic evolving spiking neural networks (ipSNN) that incorporate probability learning parameters. ipSNN utilize a quantum inspired evolutionary optimization algorithm to optimize the probability parameters as these algorithms belong to the class of estimation of distribution algorithms (EDA). Both spikes and input features in ipESNN are represented as quantum bits being in a superposition of two states (1 and 0) defined by a probability density function. This representation allows for the state of an entire ipESNN at any time to be represented probabilistically in a quantum bit register and probabilistically optimised until convergence using quantum gate operators and a fitness function. The proposed ipESNN is a promising framework for both engineering applications and brain data modeling as it offers faster and more efficient feature selection and model optimization in a large dimensional space in addition to revealing new knowledge that is not possible to obtain using other models. Further development of ipESNN are the neuro-genetic models – ipESNG, that are introduced too, along with open research questions.

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References

  1. Abbott, L.F., Sacha, B.: Synaptic plasticity: taming the beast. Nature Neuroscience 3, 1178–1183 (2000)

    Article  Google Scholar 

  2. Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cognitive Science 9, 147–169 (1985)

    Article  Google Scholar 

  3. Arbib, M. (ed.): The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (2003)

    MATH  Google Scholar 

  4. Belatreche, A., Maguire, L.P., McGinnity, M.: Advances in Design and Application of Spiking Neural Networks. Soft Comput. 11(3), 239–248 (2006)

    Article  MathSciNet  Google Scholar 

  5. Benuskova, L., Kasabov, N.: Comput. Neurogenetic Modelling. Springer, NY (2007)

    Google Scholar 

  6. Bershadskii, A., et al.: Brain neurons as quantum computers: in vivo support of background physics. Reports of the Bar-Ilan University, Israel 1-12 (2003)

    Google Scholar 

  7. Brette, R., et al.: Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience 23(3), 349–398 (2007)

    Article  MathSciNet  Google Scholar 

  8. Castellani, M.: ANNE - A New Algorithm for Evolution of ANN Classifier Systems. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 3294–3301 (2006)

    Google Scholar 

  9. Dayan, P., Hinton, G.E.: Varieties of Helmholtz machines. Neural Networks 9, 1385–1403 (1996)

    Article  MATH  Google Scholar 

  10. Dayan, P., Hinton, G.E., Neal, R., Zemel, R.S.: The Helmholtz machine. Neural Computation 7, 1022–1037 (1995)

    Google Scholar 

  11. Defoin-Platel, M., Schliebs, S., Kasabov, N.: Quantum-inspired Evolutionary Algorithm: A multi-model EDA. IEEE Trans. Evolutionary Computation (in print, 2009)

    Google Scholar 

  12. Deutsch, D.: Quantum computational networks. Proceedings of the Royal Society of London A(425), 73–90 (1989)

    Google Scholar 

  13. Ezhov, A., Ventura, D.: Quantum neural networks. In: Kasabov, N. (ed.) Future Directions for Intelligent Systems and Information Sciences. Springer, Heidelberg (2000)

    Google Scholar 

  14. Gerstner, W., Kistler, W.M.: Spiking Neuron Models. Cambridge Univ. Press, Cambridge (2002)

    MATH  Google Scholar 

  15. Gerstner, W.: What’s different with spiking neurons? In: Mastebroek, H., Vos, H. (eds.) Plausible Neural Networks for Biological Modelling, pp. 23–48. Kl. Ac. Publ., Dorchet (2001)

    Google Scholar 

  16. Guyon, I., et al. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  17. Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. on Evolutionary Computation, 580–593 (2005)

    Google Scholar 

  18. Hey, T.: Quantum computing: an introduction. Comp. & Control Eng. J. 10(6) (1999)

    Google Scholar 

  19. Hinton, G.E., Dayan, P., Frey, B.J., Neal, R.: The wake-sleep algorithm for unsupervised neural networks. Science 268, 1158–1161 (1995)

    Article  Google Scholar 

  20. Hirvensalo, M.: Quantum computing. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  21. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982)

    Article  MathSciNet  Google Scholar 

  22. Huguenard, J.R.: Reliability of axonal propagation: The spike doesn’t stop here. PNAS 97(17), 9349–9350 (2000)

    Article  Google Scholar 

  23. Izhikevich, E., Desai, N.: Relating STDP to BCM. Neural Comp. 15, 1511–1523 (2003)

    Article  MATH  Google Scholar 

  24. Izhikevich, E.: Simple model of spiking neurons. IEEE Tr. NN 14(6), 1569–1572 (2003)

    MathSciNet  Google Scholar 

  25. Izhikevich, E.: Which model to use for cortical spiking neurons? IEEE TrNN 15(5), 1063–1070 (2004)

    Google Scholar 

  26. Kasabov, N.: Evolving Connectionist Systems: The Knowl. Eng. Appr. Springer, Heidelberg (2007)

    Google Scholar 

  27. Kasabov, N.: Integrative Connectionist Learning Systems Inspired by Nature: Current Models, Future Trends and Challenges. Natural Computing (January 2008)

    Google Scholar 

  28. Kasabov, N.: Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities. In: Duch, W., Manzduk, J. (eds.) Challenges in Computational Intelligence, pp. 193–219. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  29. Kasabov, N.: Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach. IEEE Computational Intelligence Magazine 3(3), 23–37 (2008)

    Article  Google Scholar 

  30. Kasabov, N.: Found. of neural networks, fuzzy systems and knowl. eng. MIT Press, Cambridge (1996)

    Google Scholar 

  31. Katsumata, S., Sakai, K., Toujoh, S., Miyamoto, A., Nakai, J., Tsukada, M., Kojima, H.: Analysis of synaptic transmission and its plasticity by glutamate receptor channel kinetics models and 2-photon laser photolysis. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506. Springer, Heidelberg (2009)

    Google Scholar 

  32. Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Heidelberg (1997)

    MATH  Google Scholar 

  33. Kistler, G., Gerstner, W.: Spiking Neuron Models - Single Neurons, Populations, Plasticity. Cambridge Univ. Press, Cambridge (2002)

    MATH  Google Scholar 

  34. Maass, W., Bishop, C. (eds.): Pulsed Neural Networks. MIT Press, Cambridge (1999)

    Google Scholar 

  35. Pavlidis, N.G., Tasoulis, O.K., Plagianakos, V.P., Nikiforidis, G., Vrahatis, M.N.: Spiking neural network training using evolutionary algorithms. In: Proceedings IEEE International Joint Conference on neural networks, vol. 4, pp. 2190–2194 (2005)

    Google Scholar 

  36. Pfister, J.P., Barber, D., Gerstner, W.: Optimal Hebbian Learning: a Probabilistic Point of View. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 92–98. Springer, Heidelberg (2003)

    Google Scholar 

  37. Sander, M., Bohte, H.A., La Poutré, J.N.: Error-Backpropagation in Temporally Encoded Networks of Spiking Neurons. Neurocomputing 48(1-4), 17–37 (2002)

    Article  MATH  Google Scholar 

  38. Sander, M., Bohte, J.N.: Applications of spiking neural networks. Information Processing Letters 95(6), 519–520 (2005)

    Article  Google Scholar 

  39. Schliebs, S., Defoin-Platel, M., Kasabov, N.: Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506. Springer, Heidelberg (2009)

    Google Scholar 

  40. Soltic, Wysoski, S., Kasabov, N.: Evolving spiking neural networks for taste recognition. In: Proc. WCCI 2008, Hong Kong. IEEE Press, Los Alamitos (2008)

    Google Scholar 

  41. Specht, D.F.: Enhancements to probabilistic neural networks. In: Proc. Int. Joint Conference on Neural Networks, June 1992, vol. 1, pp. 761–768 (1992)

    Google Scholar 

  42. Tuffy, F., McDaid, L., Wong Kwan, V., Alderman, J., McGinnity, T.M., Kelly, P., Santos, J.: Spiking Neuron Cell Based on Charge Coupled Synapses. In: Proc. IJCNN, Vancouver (2006)

    Google Scholar 

  43. Ventura, D., Martinez, T.: Quantum associative memory. Information Sciences 124(1-4), 273–296 (2000)

    Article  MathSciNet  Google Scholar 

  44. Verstraeten, D., Schrauwen, B., Stroobandt, D., Van Campenhout, J.: Isolated word recog. with the Liquid State Machine: a case study. Inf. Proc. Letters 95(6), 521–528 (2005)

    Article  Google Scholar 

  45. 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 

  46. Wysoski, S., Benuskova, L., Kasabov, N.: On-line learning with structural adaptation in a network of spiking neurons for visual pattern recognition. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 61–70. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  47. Wysoski, S., Benuskova, L., Kasabov, N.: Brain-like Evolving Spiking Neural Networks for Multimodal Information. In: Proc. ICONIP 2007, Kitakyushu. LNCS. Springer, Heidelberg (2007)

    Google Scholar 

  48. Yadav, A., Mishra, D., Yadav, R.N., Ray, S., Kalra, P.K.: Time-series prediction with single integrate-and-fire neuron. Applied Soft Computing 7(3), 739–745 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. Knowledge Engineering and Discovery Research Institute, KEDRI, Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

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  1. Nikola Kasabov
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Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland

    Jacek Koronacki  & Sławomir T. Wierzchoń  & 

  2. Woodward Hall 430C, University of North Carolina, 9201 University City Blvd., N.C. 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Kasabov, N. (2010). Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05179-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-05179-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05178-4

  • Online ISBN: 978-3-642-05179-1

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