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Brain stem modeling at a system level - chances and limitations

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The topic of brain stem computational simulation still seems understudied in contemporary scientific literature. Current advances in neuroscience leave the brain stem as one of the least known parts of the human central nervous system. Brain stem lesions are particularly damaging to the most important physiological functions. Advances in brain stem modeling may influence important issues within the core of neurology, neurophysiology, neurosurgery, and neurorehabilitation. Direct results may include both development of knowledge and optimization and objectivization of clinical practice in the aforementioned medical areas. Despite these needs, progress in the area of computational brain stem models seems to be too slow. The aims of this paper are both to recognize the strongest limitations in the area of computational brain stem simulations and to assess the extent to which current opportunities may be exploited. Despite limitations, the emerging view of the brain stem provided by its computational models enables a wide repertoire of functions, including core dynamic behavior.
Rocznik
Strony
art. no. 20180015
Opis fizyczny
Bibliogr. 36 poz., rys.
Twórcy
  • Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
  • Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University – Neurocognitive Laboratory, Torun, Poland
autor
  • Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University – Neurocognitive Laboratory, Torun, Poland
  • Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Torun, Poland
Bibliografia
  • [1] Urban PP, Caplan RS, editors. Brain stem disorders. Heidelberg: Springer, 2011.
  • [2] Caplan LR, Hopf HC, editors. Brain-stem localization and function. Heidelberg: Springer, 1993.
  • [3] Kilmer W, McCulloch W, Blum J. A model of the vertebrate central command system. Int J Man-Machine Studies 1969;1:279-309.
  • [4] Kilmer W. A command computer for complex autonomous systems. Neurocomputing 1997;17:47-59.
  • [5] Dunin-Barkowski WL, Lovering AT, Orem JM, Baekey DM, Dick TE, Rybak IA, et al. L-plotting – A method for visual analysis of physiological experimental and modeling multi-component data. Neurocomputing 2010;74:328-36.
  • [6] Babadi B. Stimulus transmission by tonic and burst responses in a minimal model of thalamic circuit. Neurocomputing 2004;58-60:7-12.
  • [7] Shin J. Towards computational and robotic modelling of animal cognition and behavior. Neurocomputing 2002;44-46:985-92.
  • [8] Gray RT, Fung CK, Robinson PA. Stability of small-world networks of neural populations. Neurocomputing. 1999;72(7-9):1565-1574.
  • [9] Humphries MD, Gurney KN, Prescott TJ. The brain stem reticular formation is a small world not scale free network. Proc Biol Sci 2006;273:503-11.
  • [10] Merker B. Consciousness without a cerebral cortex: a challenge for neuroscience and medicine. Behav Brain Sci 2004;30:63-134.
  • [11] Olmsted DD. The recticular formation as a multi-valued logic neural network. Proc Int Joint Conf Neural Networks 1990;1:619-24.
  • [12] O’Reilly RC, Munakata Y. Computational explorations in cognitive neuroscience. Understanding the mind by simulating the brain. Cambridge: MIT Press, 2000.
  • [13] Lindsey BG, Rybak IA, Smith JC. Computational models and emergent properties of respiratory neural networks. Compr Physiol 2012;2:1619-70.
  • [14] Humphries MD, Gurney K, Prescott TJ. Is there an integrative center in the vertebrate brain-stem? A robotic evaluation of a model of the reticular formation viewed as an action selection device. Adaptive Behav 2005;13:97-113.
  • [15] Smith JC, Abdala AP, Koizumi H, Rybak IA, Paton JF. Spatial and functional architecture of the mammalian brain stem respiratory network: a hierarchy of three oscillatory mechanisms. J Neurophysiol 2007;98:3370-87.
  • [16] Lee J, Fietkiewicz Ch. Pattern variability in a computational model of respiratory rhythm generation. BMC Neurosci 2012;13:139.
  • [17] Shevtsova NA, Manzke T, Molkov YI, Bischoff A, Smith JC, Rybak IA, et al. Computational modelling of 5-HT receptor-mediated reorganization of the brain stem respiratory network. Eur J Neurosci 2011;34:1276-91.
  • [18] Trussel L. Cellular mechanisms for information coding in auditory brain stem nuclei. In: Oertel D, Fay RR, Popper AN, editors. Integrative functions in the mammalian auditory pathway. New York: Springer, 2002:72-98.
  • [19] Duch W, Nowak W, Meller J, Osiński G, Dobosz K, Mikołajewski D, et al. Computational approach to understanding autism spektrum disorders. Comput Sci 2012;13:247-61.
  • [20] Duch W, Nowak W, Meller J, Osiński G, Dobosz K, Mikołajewski D, et al. Consciousness and attention in autism spectrum disorders. In Proceedings of the Cracow Grid Workshop 2010;2011:202-11.
  • [21] Wójcik GM, Mikołajewski D, Dobosz K, Nowak W, Osiński G, Meller J, et al. The 11th Cracow Grid Workshop (CGW’11), Kraków. Poster: three-stage neurocomputational modelling using emergent and genesis software. 7th-9 th November 2011.
  • [22] Körner E, Gewaltig M-O, Körner U, Richter A, Rodemann T. A model of computation in neocortical architecture. Neural Networks 1999;2:989-1005.
  • [23] Philips AJK, Robinson PA. A quantitative model of sleep-wake dynamics based on the physiology of the brain stem ascending arousal system. J Biol Rhythms 2007;22:167-79.
  • [24] Humphries MD, Gurney K. A means to an end: validating models by fitting experimental data. Neurocomputing 2007;70:1892-906.
  • [25] Butera RJ Jr., Johnson SM, DelNegro CA, Rinzel J, Smith JC. Dynamics of excitatory networks of bursting pacemaking neurons: modeling and experimental studies of the respiratory central pattern generator. Neurocomputing 2000;32:323-30.
  • [26] Kosmidis EK, Vibert J-F. A model of respiration rhythmogenesis bridging network and pacemaker theories. Neurocomputing 2001;38-40:733-39.
  • [27] Rybak IA, Paton JF, Rogers RF, St.-John WM. Generation of the respiratory rhythm: state-dependency and switching. Neurocomputing 2002;44-46:605-14.
  • [28] Szalisznyó K, Zalányi L. Role of hyperpolarization-activated conductances in the auditory brain stem. Neurocomputing 2004;58-60:401-7.
  • [29] Li L, Xia Y, Jelfs B, Cao J, Mandic DP. Modelling of brain consciousness based on collaborative adaptive filters. Neurocomputing 2012;76:36-43.
  • [30] Filippov IV, Gladyshev AV, Williams WC. Role of infraslow (0-0.5 Hz) potential oscillations in the regulation of brain stress response by the locus coeruleus system. Neurocomputing 2002;44-46:795-808.
  • [31] Bai S, Loo C, Al Abed A, Dokos S. A computational model of direct brain excitation induced by electroconvulsive therapy: comparison among three conventional electrode placements. Brain Stimul 2012;5:408-21.
  • [32] Dobosz K, Duch W. Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Networks 2010;23:487-96.
  • [33] Duch W, Dobosz K. Visualization for understanding of neurodynamical systems. Cognitive Neurodyn 2011;5:145-60.
  • [34] Prats-Galino A, Soria G, de Notaris M, Puig J, Pedraza S. Functional anatomy of subcortical circuits issuing from or integrating at the human brain stem. Clin Neurophysiol 2012;123:4-12.
  • [35] Laigle-Donadey F, Doz F, Delattre JY. Brain stem tumors. Handb Clin Neurol 2012;105:585-605.
  • [36] Hurley RA, Flashman LA, Chow TW, Taber KH. The brain stem: anatomy, assessment, and clinical syndromes. J Neuropsych Clin Neurosci 2010;22:1-7.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3755a144-e1dd-4bdd-b571-44085d0e93bb
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