Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Understanding Nature Through the Symbiosis of Information Science, Bioinformatics, and Neuroinformatics Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

60. Brain, Gene, and Quantum Inspired Computational Intelligence

verfasst von : Nikola Kasabov

Erschienen in: Springer Handbook of Bio-/Neuroinformatics

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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; generic CI methods being 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, gene inspired, and quantum inspired CI. The main contribution of the chapter, however, is the introduction of methods inspired by the integration of principles from several levels of information processing, namely:
1.
A computational neurogenetic model that in one model combines gene information related to spiking neuronal activities.
 
2.
A general framework of a quantum spiking neural network (SNN) model.
 
3.
A general framework of a quantum computational neurogenetic model (CNGM).
 
Many open questions and challenges are discussed, along with directions for further research.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Quantum and Biocomputing – Common Notions and Targets
Nächstes Kapitel The Brain and Creativity
Literatur
60.1.
C. Bishop: Neural Networks for Pattern Recognition (Oxford Univ. Press, Oxford, UK 1995)MATH
60.2.
N. Kasabov, L. Benuskova: Computational neurogenetics, Int. J. Theor. Comput. Nanosci. 1(1), 47–61 (2004)CrossRef
60.3.
G. Marcus: The Birth of the Mind: How a Tiny Number of Genes Creates the Complexity of the Human Mind (Basic, New York 2004)
60.4.
A. Ezhov, D. Ventura: Quantum neural networks. In: Future Directions for Intelligent Systems and Information Sciences, ed. by N. Kasabov (Springer, Berlin, Heidelberg 2000) pp. 213–234CrossRef
60.5.
A. Narayanan, T. Meneer: Quantum artificial neural network architectures and components, Inf. Sci. 128, 199–215 (2000)CrossRefMathSciNet
60.6.
D.B. Fogel: Evolutionary Computation – Toward a New Philosophy of Machine Intelligence (IEEE, New York 1995)MATH
60.7.
X. Yao: Evolutionary artificial neural networks, Int. J. Neural Syst. 4(3), 203–222 (1993)CrossRef
60.8.
R. Penrose: Shadows of the Mind. A Search for the Missing Science of Conscious (Oxford Univ. Press, Oxford 1994)
60.9.
R. Penrose: The Emperorʼs New Mind (Oxford Univ. Press, Oxford 1989)
60.10.
S. Amari, N. Kasabov: Brain-like Computing and Intelligent Information Systems (Springer, New York 1998)MATH
60.11.
60.12.
M. Arbib (Ed.): The Handbook of Brain Theory and Neural Networks (MIT, Cambridge 2003)MATH
60.13.
L. Benuskova, N. Kasabov: Towards Computational Neurogenetic Modelling (Springer, New York 2007)CrossRef
60.14.
G. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds, D.B. Rosen: Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analogue multi-dimensional maps, IEEE Trans. Neural Netw. 3(5), 698–713 (1991)CrossRef
60.15.
G. Carpenter, S. Grossberg: Pattern Recognition by Self-Organizing Neural Networks (The MIT, Cambridge, USA 1991)
60.16.
N. Kasabov: Foundations of neural networks. In: Fuzzy Systems and Knowledge Engineering (MIT Press, MA 1996)
60.17.
60.18.
E. Rolls, A. Treves: Neural Networks and Brain Function (Oxford Univ. Press, Oxford 1998)
60.19.
F. Rosenblatt: Principles of Neurodynamics (Spartan Books, New York 1962)MATH
60.20.
D.E. Rumelhart, G.E. Hinton, R.J. Williams (Eds.): Learning Internal Representations by Error Propagation, Parallel Distrib, Processing: Explorations in the Microstructure of Cognition (MIT/Bradford Books, Cambridge 1986)
60.21.
G.A. Rummery, M. Niranjan: On-line Q-learning Using Connectionist System (Cambridge Univ. Press, Cambridge 1994), 166 pp., CUED/F-INENG/TR
60.22.
S. Schaal, C. Atkeson: Constructive incremental learning from only local information, Neural Comput. 10, 2047–2084 (1998)CrossRef
60.23.
60.24.
J.G. Taylor: The Race for Consciousness (MIT, Cambridge 1999)
60.25.
N. Kasabov: Evolving fuzzy neural networks – algorithms, applications and biological motivation. In: Methodologies for the Conception, Design and Application of Soft Computing, ed. by T. Yamakawa, G. Matsumoto (World Scientific, Singapore 1998) pp. 271–274
60.26.
B. Fritzke: A growing neural gas network learns topologies, Adv. Neural Inf. Process. Syst. 7, 625–632 (1995)
60.27.
60.28.
J. Freeman, D. Saad: On-line learning in radial basis function networks, Neural Comput. 9(7), 1601 –1622 (1997)CrossRef
60.29.
T. Poggio: Regularization theory, radial basis functions and networks. In: From Statistics to Neural Networks: Theory and Pattern Recognition Applications, NATO ASI Series, Vol. 136, ed. by V. Cherkassky, J.H. Friedman, H. Wechsler (NATO, Les Arcs 1994) pp. 83–104
60.30.
N. Kasabov: Evolving Connectionist Systems: The Knowledge Engineering Approach (Springer, London 2007)MATH
60.31.
W. Maass, C.M. Bishop (Eds.): Pulsed Neural Networks (The MIT, Cambridge 1999)MATH
60.32.
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)CrossRef
60.33.
N. Kasabov: Evolving fuzzy neural networks for on-line supervised/unsupervised, knowledge – based learning, SMC B: Cybern. 31(6), 902–918 (2001)
60.34.
T. Yamakawa, H. Kusanagi, E. Uchino, T. Miki: A new effective algorithm for neo fuzzy neuron model, Proc. Fifth IFSA World Congr. (IFSA, Seoul, Korea 1993) pp. 1017–1020
60.35.
60.36.
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)CrossRefMATH
60.37.
W. Gerstner, W.M. Kistler: Spiking Neuron Models (Cambridge Univ. Press, Cambridge 2002)CrossRefMATH
60.38.
A. Destexhe: Spike-and-wave oscillations based on the properties of GABAB receptors, J. Neurosci. 18, 9099–9111 (1998)
60.39.
S. Wysoski, L. Benuskova, N. Kasabov: On-line learning with structural adaptation in a network of spiking neurons for visual pattern recognition, Artificial Neural Networks – ICANN 2006 4131, 61–70 (2006)CrossRef
60.40.
C. Brown, M. Shreiber, B. Chapman, G. Jacobs: Information science and bioinformatics. In: Future Directions of Intelligent Systems and Information Sciences, ed. by N. Kasabov (Physica, Heidelberg 2000) pp. 251–287CrossRef
60.41.
D.S. Dimitrov, I. Sidorov, N. Kasabov: Computational biology. In: Handbook of Theoretical and Computational Nanotechnology, Vol. 1, ed. by M. Rieth, W. Schommers (American Scientific, Stevenson Ranch 2004), Chap. 21
60.42.
Z. Chan, N. Kasabov, L. Collins: A two-stage methodology for gene regulatory network extraction from time-course gene expression data, Expert Syst. Appl. 30(1), 59–63 (2006)CrossRef
60.43.
H. Chin, S. Moldin (Eds.): Methods in Genomic Neuroscience (CRC, Boca Raton 2001)
60.44.
N. Kasabov, S.H. Chan, V. Jain, I. Sidirov, S.D. Dimitrov: Gene Regulatory Network Discovery from Time-Series Gene Expression Data – A Computational Intelligence Approach, LNCS 3316, 1344–1353 (2004)
60.45.
60.46.
60.47.
60.48.
R.P. Feynman, R.B. Leighton, M. Sands: The Feynman Lectures on Physics (Addison-Wesley Publishing Company, Massachusetts 1965)MATH
60.49.
T. Hey: Quantum computing: An introduction. In, Comput. Control Eng. J. 10(3), 105–112 (1999)CrossRef
60.50.
60.51.
J.-S. Jang, K.-H. Han, J.-H. Kim: Quantum-inspired evolutionary algorithm-based face verification, LNCS 2724, 2147–2156 (2003)MATH
60.52.
S.C. Kak: Quantum Neural Computation, Research Report (Louisiana State Univ., Baton Rouge 1995)
60.53.
M. Brooks: Quantum Computing and Communications (Springer, Berlin, Heidelberg 1999)CrossRefMATH
60.54.
L.K. Grover: A fast quantum mechanical algorithm for database search, STOC ʼ96: Proc. Twenty-Eighth Ann. ACM Symp. Theory Comput. (ACM, New York, USA 1996) pp. 212–219CrossRef
60.55.
K.-H. Han, J.-H. Kim: Quantum-inspired evolutionary algorithms with a new termination criterion, H gate, and two phase scheme, IEEE Trans. Evol. Comput. 8(2), 156–169 (2004)CrossRef
60.56.
G.E. Hinton: Connectionist learning procedures, Artif. Intell. 40, 185–234 (1989)CrossRef
60.57.
M.A. Perkowski: Multiple-valued quantum circuits and research challenges for logic design and computational intelligence communities, IEEE Comput. Intell. Soc. Mag. 2005, 6–12 (2005)
60.58.
P.W. Shor: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM J. Comput. 26, 1484–1509 (1997)MathSciNetCrossRefMATH
60.59.
L. Spector: Automatic Quantum Computer Programming: A Genetic Programming Approach (Kluwer Academic, Boston 2004)MATH
60.60.
J. Liu, W. Xu, J. Sun: Quantum-Behaved Particle Swarm Optimization with Mutation Operator, 17th IEEE Int. Conf. Tools Artif. Intell. (ICTAIʼ05) (2005)
60.61.
60.62.
X.-Y. Tsai, H.-C. Huang, S.-J. Chuang: Quantum NN vs. NN in signal recognition, in: ICITAʼ05, Proc. Third Int. Conf. Inf. Technol. Appl. (ICITAʼ05), Vol. 2 (IEEE Computer Society, Washington, DC, USA 2005) pp. 308–312
60.63.
G.K. Venayagamoorthy, S. Gaurav: Quantum-inspired evolutionary algorithms and binary particle swarm optimization for training MLP and SRN neural networks, J. Theor. Comput. Nanosci. 2, 561–568 (2005)CrossRef
60.64.
N. Kouda, N. Matsui, H. Nishimura, F. Peper: Qu-bit neural network and its learning efficiency, Neural Comput. Appl. 14, 114–121 (2005)CrossRef
60.65.
D. Ventura, T. Martinez: Quantum associative memory, Inf. Sci. Inf. Comput. Sci. 124, 273–296 (2000)MathSciNet
60.66.
D. Ventura: Implementing competitive learning in a quantum system. In, Proc. Int. Jt. Conf. Neural Netw. (IEEE 1999)
60.67.
G. Xie, Z. Zhuang: A quantum competitive learning algorithm, Liangzi Dianzi Xuebao/Chin. J. Quantum Electron. (China) 20, 42–46 (2003)
Metadaten
Titel
Brain, Gene, and Quantum Inspired Computational Intelligence
verfasst von
Nikola Kasabov
Copyright-Jahr
2014
Buch
Springer Handbook of Bio-/Neuroinformatics

Print ISBN: 978-3-642-30573-3

Electronic ISBN: 978-3-642-30574-0

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-30574-0_60