Summary
This chapter discusses opportunities and challenges for the creation of methods of computational intelligence (CI) and more specifically – artificial neural networks (ANN), inspired by principles at different levels of information processing in the brain: cognitive-, neuronal-, genetic-, and quantum, and mainly, the issues related to the integration of these principles into more powerful and accurate CI methods. It is demonstrated how some of these methods can be applied to model biological processes and to improve our understanding in the subject area, along with other – being generic CI methods applicable to challenging generic AI problems. The chapter first offers a brief presentation of some principles of information processing at different levels of the brain, and then presents brain-inspired, geneinspired and quantum inspired CI. The main contribution of the chapter though is the introduction of methods inspired by the integration of principles from several levels of information processing, namely: (1) a computational neurogenetic model, that combines in one model gene information related to spiking neuronal activities; (2) a general framework of a quantum spiking neural network model; (3) a general framework of a quantum computational neuro-genetic model. Many open questions and challenges are discussed, along with directions for further research.
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References
Amari S, Kasabov N (1998) Brain-like Computing and Intelligent Information Systems. Springer Verlag, New York
Arbib M (1987) Brains, Machines and Mathematics. Springer-Verlag, Berlin.
Arbib M (eds) (2003) The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, MA.
Benuskova, L. and N. Kasabov (2007) Computational Neurogenetic Modelling, Springer, New York.
Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford, UK
Brooks, M. (1999) Quantum computing and communications, Springer Verlag, Berlin/Heidelberg.
Brown, C., Shreiber, M., Chapman, B., and Jacobs, G. (2000) Information science and bioinformatics, in: N.Kasabov, ed, Future directions of intelligent systems and information sciences, Physica Verlag (Springer Verlag), 251-287
Carpenter G, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1991) Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analogue multi-dimensional maps. IEEE Trans of Neural Networks 3(5): 698-713.
Carpenter G, Grossberg S (1991) Pattern recognition by self-organizing neural networks. Massachusetts. The MIT Press, Cambridge, MA, U.S.A.
Chan, Z., N.Kasabov, Lesley Collins (2006) A Two-Stage Methodology for Gene Regulatory Network Extraction from Time-Course Gene Expression Data, Expert Systems with Applications: An International Journal (ISSN: 0957-4174), Special issue on Intelligent Bioinformatics Systems, 59-63.
Chin, H., Moldin, S. (eds) (2001) Methods in Genomic Neuroscience, CRC Press.
Collin P. Williams, Scott H. Clearwater (1998) Explorations in Quantum Computing, ISBN: 038794768X, Berlin, Germany: Springer-Verlag.
Destexhe A (1998) Spike-and-wave oscillations based on the properties of GABA B receptors. J Neurosci 18: 9099-9111.
Dimitrov, D. S, I. Sidorov and N. Kasabov (2004) Computational Biology, in: M. Rieth and W. Sommers (eds) Handbook of Theoretical and Computational Nanotechnology, Vol. 6 American Scientific Publisher, Chapter 1.
Ezhov, A. and D. Ventura(2000) Quantum neural networks, in: N. Kasabov (ed) Future Directions for Intelligent Systems and Information Sciences, Springer Verlag.
Feynman, R. P., R. B. Leighton, and M. Sands (1965) The Feynman Lectures on Physics, Addison-Wesley Publishing Company, Massachusetts.
Fogel DB (1995) Evolutionary Computation - Toward a New Philosophy of Machine Intelligence. IEEE Press, New York.
Freeman J, Saad D (1997) Online learning in radial basis function networks. Neural Computation 9 (7)
Freeman W (2000) Neurodynamics. Springer-Verlag, London.
Fritzke B(1995) A growing neural gas network learns topologies. Advances in Neural Information Processing Systems 7: 625-632.
Gerstner W, Kistler WM (2002) Spiking Neuron Models. Cambridge Univ. Press, Cambridge, MA.
Grossberg S (1969) On learning and energy - entropy dependence in recurrent and nonrecurrent signed networks. J Stat Phys 1: 319-350.
Grossberg S (1982) Studies of Mind and Brain. Reidel, Boston.
Grover, L. K. (1996) A fast quantum mechanical algorithm for data-base search, in STOC ’96: Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, New York, NY, USA, ACM Press, 212-219.
Han, K.-H. and J.-H. Kim (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization, IEEE Transactions on Evolutionary Computation, 580-593.
Haykin S (1994) Neural Networks - A Comprehensive Foundation. Prentice Hall, Engelwood Cliffs, NJ.
Hebb D (1949) The Organization of Behavior. John Wiley and Sons, New York.
Heskes TM, Kappen B (1993) Online learning processes in artificial neural networks. In: (eds) Mathematic Foundations of Neural Networks, vol. Elsevier, Amsterdam, 199-233.
Hey, T. (1999) Quantum computing: An introduction, Computing & Control Engineering Journal, Piscataway, NJ: IEEE Press, June, vol. 10, no. 3,105-112.
Hinton GE (1989) Connectionist learning procedures. Artificial Intelligence 40: 185-234.
Hogg, T. and D. Portnov (2000), Quantum optimization, Information Sciences, 128, 181-197.
Jang, J.-S., K.-H. Han, and J.-H. Kim (2003) Quantum-inspired evolutionary algorithm-based face verification, Lecture Notes in Computer Science, 2147-2156.
Kak, S.C. Quantum neural computation, Research report, Louisiana State University, Dep. Electr. and Comp. Eng., Baton Rouge, LA 70803-5901, USA
Kasabov N (1996) Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. The MIT Press, CA, MA
Kasabov N (1998) Evolving Fuzzy Neural Networks - Algorithms, Applications and Biological Motivation. In: Yamakawa T, Matsumoto G (eds) Methodologies for the conception, design and application of soft computing, World Scientific, 271-274.
Kasabov N (2001) Evolving fuzzy neural networks for online supervised/unsupervised, knowledge-based learning. IEEE Trans. SMC-part B, Cybernetics 31(6): 902-918
Kasabov N. (2007) Evolving connectionist systems: The Knowledge Engineering Approach, Springer Verlag, London, New York, Heidelberg, in print (first edition 2002)
Kasabov N, Song Q (2002) DENFIS: Dynamic, evolving neural-fuzzy inference systems and its application for time-series prediction. IEEE Trans. on Fuzzy Systems 10:144-154
Kasabov N., Chan, S. H., Jain, V., Sidirov, I., and Dimitrov S. D. (2004) Gene Regulatory Network Discovery from Time-Series Gene Expression Data - A Computational Intelligence Approach, Lecture Notes in Computer science (LNCS), Springer-Verlag, Vol. 3316, 1344-1353.
Kasabov N. and L. Benuskova (2004)Computational Neurogenetics, International Journal of Theoretical and Computational Nanoscience, Vol. 1 (1) American Scientific Publisher, 2004, 47-61.
Kohonen T (1997) Self-Organizing Maps. Springer, Verlag.
Kouda, N., N. Matsui, H. Nishimura, and F. Peper (2005) Qubit neural network and its learning efficiency, Neural Comput. Appl., 14, 114-121.
Liu, J., W. Xu, and J. Sun, Quantum-behaved particle swarm optimization with mutation operator, in 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05), 2005.
Maass W, Bishop CM (eds) (1999) Pulsed Neural Networks, The MIT Press, Cambridge, MA.
Marcus G (2004) The Birth of the Mind: How a Tiny Number of Genes Creates the Complexity of the Human Mind. Basic Books, New York.
Moody J, Darken C (1989) Fast learning in networks of locally-tuned processing units Neural Computation 1: 281-294
Narayanan, A. and T. Meneer, Quantum artificial neural network architectures and components, Information Sciences, (2000), 199-215.
Penrose, R., Shadows of the mind. A search for the missing science of consciousness, Oxford University Press, 1994.
Penrose, R., The Emperor's new mind, Oxford Univ.Press, Oxford, 1989
Perkowski, M.A. Multiple-valued quantum circuits and research challenges for logic design and computational intelligence communities, IEEE Comp.Intelligence Society Magazine, November, 2005
Platt, J (1991) A resource allocating network for function interpolation. Neural Computation 3: 213-225
Poggio T (1994) Regularization theory, radial basis functions and networks. In: From Statistics to Neural Networks: Theory and Pattern Recognition Applications. NATO ASI Series, No.136, 83-104
Pribram, K. (1993) Rethinking Neural Networks: Quantum Fields and Biological data. Proceeding of the first Appalachian Conference on behavioral Neurodynamics. Lawrence Erlbaum Associates Publishers, Hills-date new Yersy
Resconi, G. and A.J.van Der Wal (2000), A data model for the morphogenetic neuron, Int.J.General Systems,Vol.29(1), 141-149.
Resconi, G., G.J.Klir, E.Pessa (1999), Conceptual Foundations of quantum mechanics the role of evidence theory, quantum sets and modal logic. International Journal of Modern Physics C. Vol.10 No.1, 29-62
Rolls, E. and A.Treves (1998) Neural Networks and Brain Function, Oxford University Press
Rosenblatt F (1962) Principles of Neurodynamics. Spartan Books, New York.
Rumelhart DE, Hinton GE, Williams RJ (eds) (1986) Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press / Bradford Books, Cambridge, MA.
Rummery GA, Niranjan M (1994) Online Q-learning using connectionist system. Cambridge University Engineering Department, CUED/FINENG/TR, pp 166
Schaal S, Atkeson C (1998) Constructive incremental learning from only local information. Neural Computation 10: 2047-2084
Shor, P. W. (1997) Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM J. Comput., 26, 1484-1509.
Song Q, Kasabov N (2005) TWNFI - Transductive Neural-Fuzzy Inference System with Weighted Data Normalization and Its Application in Medicine. IEEE Tr Fuzzy Systems, December, Vol.13, 6, 799-808
Spector, L. (2004) Automatic Quantum Computer Programming: A Genetic Programming Approach, Kluwer Academic Publishers, 2004.
Taylor JG (1999) The Race for Consciousness. MIT Press, Cambridge, MA.
Trugenberger, C. A. (2002) Quantum pattern recognition, Quantum Information Processing, 1, pp. 471-493.
Tsai, X.-Y., H.-C. Huang, and S.-J. Chuang (2005) Quantum NN vs. NN in signal recognition, in ICITA’05: Proceedings of the Third International Conference on Information Technology and Applications (ICITA’05) Volume 2, Washington, DC, USA, IEEE Computer Society, pp. 308-312.
Vapnik V (1998) Statistical Learning Theory. John Wiley & Sons Inc.
Venayagamoorthy, G. K and Gaurav, S. (2006) Quantum-Inspired Evolutionary Algorithms and Binary Particle Swarm Optimization for Training MLP and SRN Neural Networks, Journal of Theoretical and Computational Nanoscience, January.
Ventura, D. and T. Martinez (2000) Quantum associative memory, Inf. Sci. Inf. Comput. Sci., 124, 273-296.
Ventura, D.(1999) Implementing competitive learning in a quantum system, in Proceedings of the International Joint Conference of Neural Networks, IEEE Press.
Wysoski, S., L. Benuskova and N. Kasabov (2006) Online learning with structural adaptation in a network of spiking neurons for visual pattern recognition, Proc. ICANN 2006, LNCS, Springer, Part I, Vol.413, 61-70
Xie, G. and Z. Zhuang (2003) A quantum competitive learning algorithm, Liangzi Dianzi Xuebao/Chinese Journal of Quantum Electronics (China), 20,42-46.
Yamakawa T, Kusanagi H, Uchino E, Miki T (1993) A new Effective Algorithm for Neo Fuzzy neuron Model, In: Proc. Fifth IFSA World Congress, IFSA, Seoul, Korea, pp 1017-1020.
Yao X (1993) Evolutionary artificial neural networks. Intl J Neural Systems 4(3): 203-222.
Zadeh LA (1965) Fuzzy Sets. Information and Control 8: 338-353.
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Kasabov, N. (2007). Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities. In: Duch, W., Mańdziuk, J. (eds) Challenges for Computational Intelligence. Studies in Computational Intelligence, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71984-7_9
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