Evolving Connectionist Systems: From Neuro-Fuzzy-, to Spiking- and Neuro-Genetic

  • Nikola Kasabov
Part of the Springer Handbooks book series (SHB)


This chapter follows the development of a class of neural networks (NN ) called evolving connectionist systems (ECOS ). The term evolving is used here in its meaning of unfolding, developing, changing, revealing (according to the Oxford dictionary) rather than evolutionary. The latter represents processes related to populations and generations of them. An ECOS is a neural network-based model that evolves its structure and functionality through incremental, adaptive learning and self-organization during its lifetime. In principle, it could be a simple NN or a hybrid connectionist system. The latter is a system based on neural networks that also integrate other computational principles, such as linguistically meaningful explanation features of fuzzy rules, optimization techniques for structure and parameter optimization, quantum-inspired methods, and gene regulatory networks. The chapter includes definitions and examples of ECOS such as: evolving neuro-fuzzy and hybrid systems; evolving spiking neural networks, neurogenetic systems, quantum-inspired systems, which are all discussed from the point of view of the structural and functional development of a connectionist-based model and the knowledge that it represents. Applications for knowledge engineering across domain areas, such as in bioinformatics, brain study, and intelligent machines are presented.


computational intelligence


computational neuro-genetic modeling


dynamic neuro-fuzzy inference system


dynamic eSNN


evolving connectionist system




evolving fuzzy neural network


evolving spiking neural network


evolving self-organized map


evolving Takagi–Sugeno system


functional magneto-resonance imaging


fuzzy neural network


gene regulatory network

gene/protein regulatory network


leaky integrate-and-fire


neuro-fuzzy inference system


neural network


quantum-inspired eSNN


radial basis function


self-organizing map


spike pattern association neuron


spike response model


spike-timing dependent plasticity






transductive weighted neuro-fuzzy inference system


weighted-weighted nearest neighbor


  1. [40.1]
    N. Kasabov: Evolving fuzzy neural networks – Algorithms, applications and biological motivation. In: Methodologies for the Conception, Design Application of Soft Computing, ed. by T. Yamakawa, G. Matsumoto (World Scientific, Singapore 1998) pp. 271–274Google Scholar
  2. [40.2]
    N. Kasabov: Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering (MIT, Cambridge 1996) p. 550zbMATHGoogle Scholar
  3. [40.3]
    N. Kasabov, S. Shishkov: A connectionist production system with partial match and its use for approximate reasoning, Connect. Sci. 5(3/4), 275–305 (1993)CrossRefGoogle Scholar
  4. [40.4]
    N. Kasabov: Hybrid connectionist production system, J. Syst. Eng. 3(1), 15–21 (1993)Google Scholar
  5. [40.5]
    L.A. Zadeh: Fuzzy sets, Inf. Control 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  6. [40.6]
    L.A. Zadeh: Fuzzy logic, IEEE Computer 21, 83–93 (1988)CrossRefGoogle Scholar
  7. [40.7]
    L.A. Zadeh: A theory of approximate reasoning. In: Machine Intelligence, Vol. 9, ed. by J.E. Hayes, D. Michie, L.J. Mikulich (Ellis Horwood, Chichester 1979) pp. 149–194Google Scholar
  8. [40.8]
    N. Kasabov: Incorporating neural networks into production systems and a practical approach towards realisation of fuzzy expert systems, Comput. Sci. Inf. 21(2), 26–34 (1991)Google Scholar
  9. [40.9]
    N. Kasabov: Hybrid connectionist fuzzy production systems – Towards building comprehensive AI, Intell. Autom. Soft Comput. 1(4), 351–360 (1995)CrossRefGoogle Scholar
  10. [40.10]
    N. Kasabov: Connectionist fuzzy production systems, Lect. Notes Artif. Intell. 847, 114–128 (1994)Google Scholar
  11. [40.11]
    N. Kasabov: Hybrid connectionist fuzzy systems for speech recognition and the use of connectionist production systems, Lect. Notes Artif. Intell. 1011, 19–33 (1995)Google Scholar
  12. [40.12]
    T. Yamakawa, E. Uchino, T. Miki, H. Kusanagi: A neo fuzzy neuron and its application to system identification and prediction of the system behaviour, Proc. 2nd Int. Conf. Fuzzy Log. Neural Netw. (Iizuka, Japan 1992) pp. 477–483Google Scholar
  13. [40.13]
    T. Yamakawa, S. Tomoda: A fuzzy neuron and its application to pattern recognition, Proc. 3rd IFSA Congr., ed. by J. Bezdek (Seattle, Washington 1989) pp. 1–9Google Scholar
  14. [40.14]
    T. Furuhashi, T. Hasegawa, S. Horikawa, Y. Uchikawa: An adaptive fuzzy controller using fuzzy neural networks, Proc. 5th IFSA World Congr. Seoul (1993) pp. 769–772Google Scholar
  15. [40.15]
    N. Kasabov, J.S. Kim, M. Watts, A. Gray: FuNN/2 – A fuzzy neural network architecture for adaptive learning and knowledge acquisition, Inf. Sci. 101(3/4), 155–175 (1997)CrossRefGoogle Scholar
  16. [40.16]
    N. Kasabov: Evolving fuzzy neural networks for supervised/unsupervised online knowledge–based learning, IEEE Trans. Syst. Man Cybern. B 31(6), 902–918 (2001)CrossRefGoogle Scholar
  17. [40.17]
    D. Deng, N. Kasabov: On-line pattern analysis by evolving self-organising maps, Neurocomputing 51, 87–103 (2003)CrossRefGoogle Scholar
  18. [40.18]
    N. Kasabov: Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines, Perpective in Neural Computing (Springer, Berlin, Heidelberg 2003)CrossRefzbMATHGoogle Scholar
  19. [40.19]
    M. Watts: A decade of Kasabov's evolving connectionist systems: A review, IEEE Trans. Syst. Man Cybern. C 39(3), 253–269 (2009)CrossRefGoogle Scholar
  20. [40.20]
    N. Kohonen: Self-Organizing Maps, 2nd edn. (Springer, Berlin, Heidelberg 1997)CrossRefzbMATHGoogle Scholar
  21. [40.21]
    F. Girosi: Regularization theory, radial basis functions and networks. In: From Statistics to Neural Networks, ed. by V. Cherkassky, J.H. Friedman, H. Wechsler (Springer, Heidelberg 1994) pp. 166–187CrossRefGoogle Scholar
  22. [40.22]
    G.A. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds, D.B. Rosen: Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analogue multidimensional maps, IEEE Trans. Neural Netw. 3(5), 698–713 (1991)CrossRefGoogle Scholar
  23. [40.23]
    B. Fritzke: A growing neural gas network learns topologies, Adv. Neural Inf. Process. Syst. 7, 625–632 (1995)Google Scholar
  24. [40.24]
    J. Platt: A resource allocating network for function interpolation, Neural Comput. 3, 213–225 (1991)MathSciNetCrossRefGoogle Scholar
  25. [40.25]
    N. Kasabov, Q. Song: DENFIS: Dynamic, evolving neural-fuzzy inference Systems and its application for time-series prediction, IEEE Trans. Fuzzy Syst. 10, 144–154 (2002)CrossRefGoogle Scholar
  26. [40.26]
    N. Kasabov: Evolving Connectionist Systems: The Knowledge Engineering Approach (Springer, Berlin, Heidelberg 2007)zbMATHGoogle Scholar
  27. [40.27]
    J. Bezdek: A review of probabilistic, fuzzy, and neural models for pattern recognition, J. Intell. Fuzzy Syst. 1, 1–25 (1993)CrossRefGoogle Scholar
  28. [40.28]
    J. Bezdek (Ed.): Analysis of Fuzzy Information (CRC, Boca Raton 1987)zbMATHGoogle Scholar
  29. [40.29]
    J. Bezdek: Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum, New York 1981)CrossRefzbMATHGoogle Scholar
  30. [40.30]
    R.R. Yager, D. Filev: Generation of fuzzy rules by mountain clustering, J. Intell. Fuzzy Syst. 2, 209–219 (1994)Google Scholar
  31. [40.31]
    Q. Song, N. Kasabov: NFI: A neuro-fuzzy inference method for transductive reasoning, IEEE Trans. Fuzzy Syst. 13(6), 799–808 (2005)CrossRefGoogle Scholar
  32. [40.32]
    Q. Song, N. Kasabov: TWNFI – A transductive neuro-fuzzy inference system with weighted data normalisation for personalised modelling, Neural Netw. 19(10), 1591–1596 (2006)CrossRefzbMATHGoogle Scholar
  33. [40.33]
    N. Kasabov, Y. Hu: Integrated optimisation method for personalised modelling and case studies for medical decision support, Int. J. Funct. Inf. Pers. Med. 3(3), 236–256 (2010)Google Scholar
  34. [40.34]
    N. Kasabov: Global, local and personalised modelling and profile discovery in bioinformatics: An integrated approach, Pattern Recognit. Lett. 28(6), 673–685 (2007)CrossRefGoogle Scholar
  35. [40.35]
    S. Ozawa, S. Pang, N. Kasabov: On-line feature selection for adaptive evolving connectionist systems, Int. J. Innov. Comput. Inf. Control 2(1), 181–192 (2006)Google Scholar
  36. [40.36]
    S. Ozawa, S. Pang, N. Kasabov: Incremental learning of feature space and classifier for online pattern recognition, Int. J. Knowl. Intell. Eng. Syst. 10, 57–65 (2006)Google Scholar
  37. [40.37]
    M. Watts: Evolving Connectionist Systems: Characterisation, Simplification, Formalisation, Explanation and Optimisation, Ph.D. Thesis (University of Otago, Dunedin 2004)Google Scholar
  38. [40.38]
    N.L. Mineu, A.J. da Silva, T.B. Ludermir: Evolving neural networks using differential evolution with neighborhood-based mutation and simple subpopulation scheme, Proc. Braz. Symp. Neural Netw. SBRN (2012) pp. 190–195Google Scholar
  39. [40.39]
    P. Angelov: Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems (Springer, Berlin, Heidelberg 2002)CrossRefzbMATHGoogle Scholar
  40. [40.40]
    N. Kasabov: Adaptive modelling and discovery in bioinformatics: The evolving connectionist approach, Int. J. Intell. Syst. 23, 545–555 (2008)CrossRefGoogle Scholar
  41. [40.41]
    L. Benuskova, N. Kasabov: Computational Neuro-Genetic Modelling (Springer, Berlin, Heidelberg 2007)CrossRefGoogle Scholar
  42. [40.42]
    L. Huang, Q. Song, N. Kasabov: Evolving connectionist system based role allocation for robotic soccer, Int. J. Adv. Robot. Syst. 5(1), 59–62 (2008)Google Scholar
  43. [40.43]
    N. Kasabov: Adaptation and interaction in dynamical systems: Modelling and rule discovery through evolving connectionist systems, Appl. Soft Comput. 6(3), 307–322 (2006)CrossRefGoogle Scholar
  44. [40.44]
    S. Schliebs, M. Defoin-Platel, S.P. Worner, N. Kasabov: Integrated feature and parameter optimization for evolving spiking neural networks: Exploring heterogeneous probabilistic models, Neural Netw. 22, 623–632 (2009)CrossRefGoogle Scholar
  45. [40.45]
    N. Kasabov, E. Postma, J. van den Herik: AVIS: A connectionist-based framework for integrated auditory and visual information processing, Inf. Sci. 123, 127–148 (2000)CrossRefzbMATHGoogle Scholar
  46. [40.46]
    S. Pang, T. Ban, Y. Kadobayashi, K. Kasabov: LDA merging and splitting with applications to multiagent cooperative learning and system alteration, IEEE Trans. Syst. Man Cybern. B 42(2), 552–564 (2012)CrossRefGoogle Scholar
  47. [40.47]
    H. Widiputra, R. Pears, N. Kasabov: Multiple time-series prediction through multiple time-series relationships profiling and clustered recurring trends, Lect. Notes Artif. Intell. 6635, 161–172 (2011)Google Scholar
  48. [40.48]
    D. Hebb: The Organization of Behavior (Wiley, New York 1949)Google Scholar
  49. [40.49]
    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
  50. [40.50]
    J. Hopfield: Pattern recognition computation using action potential timing for stimulus representation, Nature 376, 33–36 (1995)CrossRefGoogle Scholar
  51. [40.51]
    W. Maass: Computing with spiking neurons. In: Pulsed Neural Networks, ed. by W. Maass, C.M. Bishop (MIT, Cambridge 1998) pp. 55–81Google Scholar
  52. [40.52]
    W. Gerstner: Time structure of the activity of neural network models, Phys. Rev. E 51, 738–758 (1995)CrossRefGoogle Scholar
  53. [40.53]
    E.M. Izhikevich: Which model to use for cortical spiking neurons?, IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004)CrossRefGoogle Scholar
  54. [40.54]
    S. Thorpe, A. Delorme, R. van Ruller: Spike-based strategies for rapid processing, Neural Netw. 14(6/7), 715–725 (2001)CrossRefGoogle Scholar
  55. [40.55]
    N. Kasabov: To spike or not to spike: A probabilistic spiking neuron model, Neural Netw. 23(1), 16–19 (2010)CrossRefGoogle Scholar
  56. [40.56]
    S. Wysoski, L. Benuskova, N. Kasabov: Evolving spiking neural networks for audiovisual information processing, Neural Netw. 23(7), 819–836 (2010)CrossRefGoogle Scholar
  57. [40.57]
    D. Verstraeten, B. Schrauwen, M. d'Haene, D. Stroobandt: An experimental unification of reservoir computing methods, Neural Netw. 20(3), 391–403 (2007)CrossRefzbMATHGoogle Scholar
  58. [40.58]
    S. Song, K. Miller, L. Abbott: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity, Nat. Neurosci. 3, 919–926 (2000)CrossRefGoogle Scholar
  59. [40.59]
    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
  60. [40.60]
    N. Kasabov, K. Dhoble, N. Nuntalid, G. Indiveri: Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition, Neural Netw. 41, 188–201 (2013)CrossRefGoogle Scholar
  61. [40.61]
    A. Mohemmed, S. Schliebs, S. Matsuda, N. Kasabov: SPAN: Spike pattern association neuron for learning spatio-temporal spike patterns, Int. J. Neural Syst. 22(4), 1250012 (2012)CrossRefGoogle Scholar
  62. [40.62]
    N. Nuntalid, K. Dhoble, N. Kasabov: EEG classification with BSA spike encoding algorithm and evolving probabilistic spiking neural network, Lect. Notes Comput. Sci. 7062, 451–460 (2011)CrossRefGoogle Scholar
  63. [40.63]
    G. Indiveri, B. Linares-Barranco, T.J. Hamilton, A. van Schaik, R. Etienne-Cummings, T. Delbruck, S.-C. Liu, P. Dudek, P. Häfliger, S. Renaud, J. Schemmel, G. Cauwenberghs, J. Arthur, K. Hynna, F. Folowosele, S. Saighi, T. Serrano-Gotarredona, J. Wijekoon, Y. Wang, K. Boahen: Neuromorphic silicon neuron circuits, Front. Neurosci. 5, 5 (2011)Google Scholar
  64. [40.64]
    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
  65. [40.65]
    S. Schliebs, N. Kasabov: Evolving spiking neural networks – a survey, Evol. Syst. 4(2), 87–98 (2013)CrossRefGoogle Scholar
  66. [40.66]
    N. Kasabov, L. Benuskova, S. Wysoski: A computational neurogenetic model of a spiking neuron, Neural Netw. IJCNN'05. Proc. (2005) pp. 446–451Google Scholar
  67. [40.67]
    N. Kasabov: NeuCube EvoSpike architecture for spatio-temporal modelling and pattern recognition of brain signals, Lect. Notes Comput. Sci. 7477, 225–243 (2012)CrossRefGoogle Scholar
  68. [40.68]
    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
  69. [40.69]
    H. Nuzly, A. Hamed, S.M. Shamsuddin: Probabilistic evolving spiking neural network optimization using dynamic quantum inspired particle swarm optimization, Aust. J. Intell. Inf. Process. Syst. 11(1), 5–15 (2010)Google Scholar
  70. [40.70]
    N. Kasabov, R. Schliebs, H. Kojima: Probabilistic computational neurogenetic framework: From modelling cognitive systems to Alzheimer's disease, IEEE Trans. Auton. Ment. Dev. 3(4), 300–311 (2011)CrossRefGoogle Scholar
  71. [40.71]
    N. Kasabov (Ed.): Springer Handbook of Bio/Neuroinformatics (Springer, Berlin, Heidelberg 2014)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

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

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