Computational Approach to Understanding Autism Spectrum Disorders

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 architecture

Noise as a useful signal within the nervous system in neurorehabilitation

“…It is influenced by the fact that the areas and limits of brain plasticity are still unknown. On the contrary, even subtle changes in the neural networks' complexity (e.g., reflected in EEG abnormalities) may cause cognitive disorders, as suspected in ASD [1,2,[28][29][30]. Moreover, a lot of knowledge concerning signal processing within nervous systems remains undiscovered or even still undetectable.…”

“…This makes it difficult to differentiate between the important stimuli and the others. Of course, the aforementioned mechanisms, if confirmed, will be only a part of neuroanatomical and neurophysiologic causes of ASD and ADHD (including probably genetic, neuromodulatory, structural, and functional components) [1,2]. Even more important may be the influence of the noise to balance between variability and stability/integrity during the development of a young nervous system.…”

“…The recent dynamic developments in clinical applications of cognitive sciences [1,2], assistive technology [e.g., brain-computer interfaces (BCIs) and neuroprostheses (NPs)] [3,4], and specific interventions [e.g., deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS)] in neurorehabilitation [5,6] has proven the role of signal processing and noise for proper nervous system function. Papers by Faisal [7,8] provided a reliable classification of noise sources within the human nervous system (sensory, cellular, and motor; Figure 1).…”

Adaptation of the humanoid robot to speech disfluency therapy

“…On the contrary, we have some experience in modeling attention and consciousness in ASD [29][30][31][32]. This is the reason why a deeper investigation of ASD with the application of NAO robot will be in our future interest and research to be conducted at the Department of Neuroinformatics.…”

Autism and ADHD – Two Ends of the Same Spectrum?

“…However, these conclusions have been confirmed by more complex models of normal reading and dyslexia, and of simple repetitive movements [6,10] implemented also in the Emergent simulator. The first of these models [10] has 6 layers, representing information about orthography (6x8 units), phonology (14x14 units) and semantics (10x14 units), connected to each other via intermediate (hidden) layers of neurons. Full connectivity between each adjacent layer is assumed, with recurrent self-connections within each of these layers.…”