COMPUTATIONAL APPROACH TO UNDERSTANDING AUTISM SPECTRUM DISORDERS
Keywords:computational neuroscience, neural networks, autism, Autism Spectrum Disorders, ASD
AbstractEvery 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
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How to Cite
Duch, W., Nowak, W., Meller, J., Osiński, G., Dobosz, K., Mikołajewski, D., & Wójcik, G. M. (2012). COMPUTATIONAL APPROACH TO UNDERSTANDING AUTISM SPECTRUM DISORDERS. Computer Science, 13(2), 47. https://doi.org/10.7494/csci.2012.13.2.47