COMPUTATIONAL APPROACH TO UNDERSTANDING AUTISM SPECTRUM DISORDERS

Włodzisław Duch; Wiesław Nowak; Jaroslaw Meller; Grzegorz Osiński; Krzysztof Dobosz; Dariusz Mikołajewski; Grzegorz Marcin Wójcik

doi:10.7494/csci.2012.13.2.47

Authors

Włodzisław Duch Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun
Wiesław Nowak Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun
Jaroslaw Meller Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun
Grzegorz Osiński Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun
Krzysztof Dobosz Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun
Dariusz Mikołajewski Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun
Grzegorz Marcin Wójcik Institute of Computer Science, Maria Curie-Sklodowska University, Pl. Marii Curie-Skłodowskiej 1, 20-031, Lublin

DOI:

https://doi.org/10.7494/csci.2012.13.2.47

Keywords:

computational neuroscience, neural networks, autism, Autism Spectrum Disorders, ASD

Abstract

Every year the prevalence of Autism Spectrum of Disorders (ASD) is rising. Is there a unifying mechanism of various ASD cases at the genetic, molecular, cellular or systems level? The hypothesis advanced in this paper is focused on neural dysfunctions that lead to problems with attention in autistic people. Simulations of attractor neural networks performing cognitive functions help to assess system long-term neurodynamics. The Fuzzy Symbolic Dynamics (FSD) technique is used for the visualization of attractors in the semantic layer of the neural model of reading. Large-scale simulations of brain structures characterized by a high order of complexity requires enormous computational power, especially if biologically motivated neuron models are used to investigate the inﬂuence of cellular structure dysfunctions on the network dynamics. Such simulations have to be implemented on computer clusters in a grid-based architectures