Simple cyclic movements as a distinct autism feature - computational approach

• Autorzy: Dobosz K. , Mikołajewski D. , Wójcik G. M. , Duch W.
• Języki publikacji: EN

Abstrakty

EN

A diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders (ASD). Each year, the percentage of children diagnosed with ASD is growing. One common diagnostic feature in individuals with ASD is the tendency to exhibit atypical simple cyclic movements.The motor brain activity seems to generate a periodic attractor state that is hard to escape. Despite numerous studies, scientists and clinicians do not know exactly if ASD is a result of a simple yet general mechanism or of a complex set of mechanisms (either on the neural, molecular and system levels). Simulations using the biologically - relevant neural network model presented here may help to reveal the simplest mechanisms that may be responsible for specific behavior. Abnormal neural fatigue mechanisms may be responsible for motor symptoms as well as many (or perhaps all) of the other symptoms observed in ASD.

Słowa kluczowe

EN

computational neuroscience; neural networks; attractor networks; motor control; repetitive movements; ion channels; autism spectrum disorder; ASD; emergent simulator; GENESIS simulator

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2013
• Tom: Vol. 14 (3)
• Strony: 475--489
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Dobosz K.

• Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland,

autor

Mikołajewski D.

• Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, ul. Kopernika 1, 85-074 Bydgoszcz, Poland
• Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

autor

Wójcik G. M.

• Institute of Computer Science, Maria Curie-Sklodowska University, Pl. Marii Curie-Sklodowskiej 1, 20-031, Lublin, Poland

autor

Duch W.

• Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

Bibliografia

• [1] Aisa B.,O’Reilly R.: The emergent neural modeling system. Neural Networks, 21:1045–1212, 2008.
• [2] Baron Cohen S., Scott F., Allison C., Williams J.,Bolton P., Matthews F., Brayne C.: Prevalence of autism-spectrumconditions. UK school-based population study The British Journal of Psychiatry, 194(6):500–509, 2009.
• [3] Bilder R.M., Sabb F.W., Cannon T.D., London E.D., Jentsch J.D., Parker D.S., Poldrack R.A., Evans C., Freimer N.B.: Phenomics: the systematic study of phenotypesonagenome-widescale. Neuroscience, 164(1), 2009.
• [4] Bilder R.M., Sabb F.W., Parker D.S., Kalar D., Chu W.W., Fox J., FreimerN.B., PoldrackR.A.: Cognitive ontologies for neuropsychiatric phenomics research. Cognitive Neuropsychiatry,14(4–5):419–450, 2009.
• [5] Campos F.,Calado J.: Approaches to human armmovement control–areview. Annual reviews in Control, 33:69–77, 2008.
• [6] Dobosz K., Duch W.: Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Networks, 23(4):487–496, 2010.
• [7] Duch W., Nowak W., Meller J., Osinski G., Dobosz K., Mikolajewski D., Wojcik G.M.: Consciousness and attention in autism spectrum disorders. In Proc. of Cracow Grid Workshop 2010, pp.202–211, 2011.
• [8] Duch W., Nowak W., Meller J., Osinski G., Dobosz K., Mikolajewski D.,Wojcik G.M.: Computational approach to understanding autism spectrum disorders. Computer Science,13(2):47–61, 2012.
• [9] Duch W., Dobosz K.: Visualization for understanding of neurodynamical systems. Cognitive Neurodynamics, 5(2):145–160, 2011.
• [10] Geshwind D.H.: Autism: Many genes, common pathways? Cell, 135:391–395, 2008.
• [11] Lennon S., Stokes M.: Pocketbook of Neurological Physiotherapy. ElsevierScience, 2009.
• [12] Naude J., Paz J.,Berry H., Delord B.: A theory of ratecoding control by intrinsic plasticity effects. PLoS Comput Biol, 8(1), 2012. e1002349.
• [13] O’Reilly R.C., Munakata Y.: Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MITPress, Cambridge, Massachusetts, 2000.
• [14] WojcikG.M.: Electrical parameters influence on the dynamics of the hodgkin-huxley liquid state machine. Neurocomputing,(79):68–78, 2012.
• [15] Wojcik G.M.:Self-organising criticality inthesimulated models of theratcortical microcircuits. Neurocomputing, (79):61–67, 2012.
• [16] Wojcik G.M., Kaminski W.A.: Liquid computations and large simulations of the mammalian visual cortex. In Computational Science – ICCS 2006, volume 3992 of Lecture Notes in Computer Science, pp.94–101. Springer, 2006.
• [17] Wojcik G.M.,Kaminski W.A.: Self-organised criticality as a function of connections number in the model of the rat somatosensory cortex. In Computational Science – ICCS 2008, vol.5101 of Lecture Notes in Computer Science, pp.620–629. Springer, 2008.
• [18] Wojcik G.M., Kaminski W.A., Matejanka P.: Self-organised criticality in a model of the rat somatosensory cortex. In Parallel Computing Technologies, vol.4671 of Lecture Notes in Computer Science, pp.468–475. Springer, 2007.
• [19] Zimmerman A.W.: Autism: Current theories and evidence. Humana Press,Totowa, NJ, 2008.