SIMPLE CYCLIC MOVEMENTS AS A DISTINCT AUTISM FEATURE - COMPUTATIONAL APPROACH
DOI:
https://doi.org/10.7494/csci.2013.14.3.475Keywords:
computational neuroscience, neural networks, attractor networks, motor control, repetitive movements, ion channels, Autism Spectrum Disorders, ASD, Emergent simulator, GENESIS simulatorAbstract
Diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders(ASD). Each year percentage of children diagnosed with ASD is growing. One common diag-nostic feature in individuals with ASD is the tendency to atypical simple cyclic movements.The motor brain activity seems to generate periodic attractor state that is hard to escape.Despite numerous studies scientists and clinicians do not know exactly if ASD is a result ofa simple but general mechanism, or a complex set of mechanisms, both on neural, molecularand system levels. Simulations using biologically relevant neural network model presentedhere may help to reveal simplest mechanisms that may be responsible for specific behavior.Abnormal neural fatigue mechanisms may be responsible for motor as well as many if notall other symptoms observed in ASD.Downloads
References
Aisa B., O’Reilly R.:. The emergent neural modeling system. Neural Networks,
:1045–1212, 2008.
Baron-Cohen S., Scott F., Allison C., Williams J., Bolton P., Matthews F., Brayne
C.:. Prevalence of autism-spectrum conditions. UK school-based population study
The British Journal of Psychiatry, 194(6):500–509, 2009.
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 phenotypes on a genome-wide scale. Neuroscience, 164(1), 2009.
Campos F., Calado J.:. Approaches to human arm movement control - a review.
Annual reviews in Control, 33:69–77, 2008.
Dobosz K., Duch W.:. Understanding neurodynamical systems via fuzzy symbolic
dynamics. Neural Networks, 23(4):487–496, 2010.
Duch W., Nowak W., Meller J., Osinski G., Dobosz K., Mikolajewski D., Włjcik
G. M.:. Consciousness and attention in autism spectrum disorders. In Proceedings
of Cracow Grid Workshop 2010, 2011. W druku.
Duch W., Nowak W., Meller J., Osinski G., Dobosz K., Mikolajewski D., Włjcik
G. M.:. Computational approach to understanding autism spectrum disorders.
Computer Science, 13(2):47–61, 2012.
lutego 2013
str.13/14
Duch W., Dobosz K.:. Visualization for understanding of neurodynamical sys-
tems. Cognitive Neurodynamics, 5(2):145–160, 2011.
Lennon S., Stokes M.:. Pocketbook of Neurological Physiotherapy. Elsevier Science,
O’Reilly R. C., Munakata Y.:. Computational Explorations in Cognitive Neuro-
science: Understanding the Mind by Simulating the Brain. MIT Press, Cambridge,
Massachusetts, 2000.
Wojcik G. M.:. Electrical parameters influence on the dynamics of the hodgkin-
huxley liquid state machine. Neurocomputing, (79):68–78, 2012.
Wojcik G. M.:. Self-organising criticality in the simulated models of the rat
cortical microcircuits. Neurocomputing, (79):61–67, 2012.
Wojcik G. M., Kaminski W. A.:. Liquid computations and large simulations of
the mammalian visual cortex. In Computational Science – ICCS 2006, volume
of Lecture Notes in Computer Science, pages 94–101. Springer, 2006.
Wojcik G. M., Kaminski W. A.:. Self-organised criticality as a function of con-
nections number in the model of the rat somatosensory cortex. In Computational
Science – ICCS 2008, volume 5101 of Lecture Notes in Computer Science, pages
–629. Springer, 2008.
Wojcik G. M., Kaminski W. A., Matejanka P.:. Self-organised criticality in a
model of the rat somatosensory cortex. In Parallel Computing Technologies,
volume 4671 of Lecture Notes in Computer Science, pages 468–475. Springer,
Zimmerman A. W.:. Autism: Current theories and evidence. Humana Press,
Totowa, NJ, 2000.
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