Fuzzy-based computational simulations of brain functions - preliminary concept

• Autorzy: Prokopowicz, P. , Mikołajewski, D.
• Języki publikacji: EN

Abstrakty

EN

Research on the computational models of the brain constitutes an important part of the current challenges within computational neuroscience. The current results are not satisfying. Despite the continuous efforts of scientists and clinicians, it is hard to fully explain all the mechanisms of a brain function. Computational models of the brain based on fuzzy logic, including ordered fuzzy numbers, may constitute another breakthrough in the aforementioned area, offering a completing position to the current state of the art. The aim of this paper is to assess the extent to which possible opportunities concerning computational brain models based on fuzzy logic techniques may be exploited both in the area of theoretical and experimental computational neuroscience and in clinical applications, including our own concept. The proposed approach can open a family of novel methods for a more effective and (neuro)biologically reliable brain simulation based on fuzzy logic techniques useful in both basic sciences and applied sciences.

Słowa kluczowe

EN

brain plasticity; computational brain model; computational neuroscience; fuzzy system; neurology

• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2016
• Tom: Vol. 12, no. 3
• Strony: 99-104
• Opis fizyczny: Bibliogr. 29 poz., rys.

Twórcy

autor

Prokopowicz, P.

• Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland

autor

Mikołajewski, D.

• Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, ul. Kopernika 1, 85-074 Bydgoszcz, Poland,
• Department of Informatics, Nicolaus Copernicus University, Toruń, Poland
• Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland

Bibliografia

• 1. Markram H. Seven challenges for neuroscience. Funct Neurol 2013;28:145–51.
• 2. Woodman MM, Pezard L, Domide L, Marmaduke Woodman M, Pezard L, Domide L, et al. Integrating neuroinformatics tools in TheVirtualBrain. Front Neuroinform 2014;8:36.
• 3. D’Angelo E, Solinas S, Garrido J, Casellato C, Pedrocchi A, Mapelli J, et al. Realistic modeling of neurons and networks: towards brain simulation. Funct Neurol 2013;28:153–66.
• 4. Carnevale NT, Hines ML. The NEURON book. Cambridge: Cambridge University Press, 2006.
• 5. Bower JM, Beeman D. The book of GENESIS: exploring realistic neural models with the general neural simulation system. New York: Springer, 1998.
• 6. O’Reilly RC, Munakata Y. Computational explorations in cognitive neuroscience. Cambridge: The MIT Press, 2000.
• 7. Faisal AA. Noise in neurons and other constraints. In: Le Novère N, editor. Computational systems neurobiology. New York: Springer, 2012.
• 8. Faisal AA, Selen LP, Wolpert DM. Noise in the nervous system. Nat Rev Neurosci 2008;9:292–303.
• 9. Parvisi J, Damasio A. Consciousness and the brainstem. Cognition 2001;79:135–59.
• 10. Balduzzi D, Tononi G. Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput Biol 2008;4:e1000091.
• 11. Seth AK, Dienes Z, Cleermans A, Overgaard M, Pessoa L. Measuring consciousness: relating behavioural and neurophysiological approaches. Trends Cognit Sci 2008;12:314–21.
• 12. Wójcik GM, Kaminski WA. Liquid state machine and its separation ability as function of electrical parameters of cell. Neurocomputing 2007;70:2593–697.
• 13. Wójcik GM. Self-organising criticality in the simulated models of the rat cortical microcircuits. Neurocomputing 2012;79:61–7.
• 14. Duch W, Dobosz K, Mikołajewski D. Autism and ADHD: two ends of the same spectrum? Lect Notes Comput Sci 2013;8226:623–30.
• 15. Dobosz K, Duch W. Visualization for understanding of neurodynamical systems. Cognit Neurodyn 2011;5:145–60.
• 16. Dobosz K, Duch W. Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Netw 2010;23:487–96.
• 17. Dobosz K, Duch W. Fuzzy symbolic dynamics for neurodynamical systems. Lect Notes Comput Sci 2008;5164:471–8.
• 18. Li H, Deklerck R, De Cuyper B, Hermanus A, Nyssen E, Cornelis J. Object recognition in brain CT-scans: knowledge-based fusion of data from multiple feature extractors. IEEE Trans Med Imaging 1995;14:212–29.
• 19. Zadeh L. The concept of a linguistic variable and its application to approximate reasoning. Springfield: National Technical Information Service, 1973.
• 20. Mamdani E, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Machine Stud 1975;7:1–15.
• 21. Raju GV, Zhou J, Kisner RA. Hierarchical fuzzy control. Int J Control 1991;54:1201–16.
• 22. Gegov A. Fuzzy networks for complex systems – a modular rule base approach. In: Studies in fuzziness and soft computing 259. New York: Springer, 2010:1–277.
• 23. Kosiński W, Prokopowicz P, Ślęzak D. Ordered fuzzy numbers. Bull Polish Acad Sci Ser Sci Math 2003;51:327–38.
• 24. Kosiński W, Prokopowicz P. Fuzziness – representation of dynamic changes, using ordered fuzzy numbers arithmetic, new dimensions in fuzzy logic and related technologies. In: Stepnicka M, Nova V, Bodenhofer U, editors. Proc. 5th EUSFLAT Conference, vol I, Ostrava, Czech Republic, September 11–14, 2007:449–56.
• 25. Kosiński W, Prokopowicz P, Kacprzak D. Fuzziness – representation of dynamic changes by ordered fuzzy numbers. In: Seising R, editor. Studies in fuzziness and soft computing. Views of fuzzy sets and systems from different perspectives. Heidelberg: Springer, 2009:243:485–508.
• 26. Prokopowicz P. Flexible and simple methods of calculations on fuzzy numbers with the ordered fuzzy numbers model. In: Rutkowski L, Korytkowski M, Scherer R, Tadeusiewicz R, Zadeh LA, Zurada JM, editors. Proc. ICAISC 2013, Part I. LNCS (LNAI). Heidelberg: Springer 2013;7894:365–75.
• 27. Kosiński W, Prokopowicz P, Rosa A. Defuzzification functionals of ordered fuzzy numbers. IEEE Trans Fuzzy Syst 2013;21:1163–9.
• 28. Prokopowicz P. Adaptation of rules in the fuzzy control system using the arithmetic of ordered fuzzy numbers. In: Proc. ICAISC 2008. LNCS (LNAxI). New York: Springer, 2008:5097:306–16.
• 29. Prokopowicz P, Malek S. Aggregation operator for ordered fuzzy numbers concerning the direction. In: Rutkowski L, Korytkowski M, Scherer R, Tadeusiewicz R, Zadeh LA, Zurada JM, editors. Proc. ICAISC 2014, Part I. LNCS (LNAI). Switzerland: Springer International Publishing, 2014;8467:267–78.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.

Identyfikatory

DOI

10.1515/bams-2016-0009