a b s t r a c tNeurodynamical systems are characterized by a large number of signal streams, measuring activity of individual neurons, local field potentials, aggregated electrical (EEG) or magnetic potentials (MEG), oxygen use (fMRI) or activity of simulated neurons. Various basis set decomposition techniques are used to analyze such signals, trying to discover components that carry meaningful information, but these techniques tell us little about the global activity of the whole system. A novel technique called Fuzzy Symbolic Dynamics (FSD) is introduced to help in understanding of the multidimensional dynamical system's behavior. It is based on a fuzzy partitioning of the signal space that defines a non-linear mapping of the system's trajectory to the low-dimensional space of membership function activations. This allows for visualization of the trajectory showing various aspects of observed signals that may be difficult to discover looking at individual components, or to notice otherwise. FSD mapping can be applied to raw signals, transformed signals (for example, ICA components), or to signals defined in the time-frequency domain. To illustrate the method two FSD visualizations are presented: a model system with artificial radial oscillatory sources, and the output layer (50 neurons) of Respiratory Rhythm Generator (RRG) composed of 300 spiking neurons.
Complex neurodynamical systems are quite difficult to analyze and understand. New type of plots are introduced to help in visualization of high-dimensional trajectories and show global picture of the phase space, including relations between basins of attractors. Color recurrence plots (RPs) display distances from each point on the trajectory to all other points in a two-dimensional matrix. Fuzzy Symbolic Dynamics (FSD) plots enhance this information mapping the whole trajectory to two or three dimensions. Each coordinate is defined by the value of a fuzzy localized membership function, optimized to visualize interesting features of the dynamics, showing to which degree a point on the trajectory belongs to some neighborhood. The variance of the trajectory within the attraction basin plotted against the variance of the synaptic noise provides information about sizes and shapes of these basins. Plots that use color to show the distance between each trajectory point and a larger number of selected reference points (for example centers of attractor basins) are also introduced. Activity of 140 neurons in the semantic layer of dyslexia model implemented in the Emergent neural simulator is analyzed in details showing different aspects of neurodynamics that may be understood in this way. Influence of connectivity and various neural properties on network dynamics is illustrated using visualization techniques. A number of interesting conclusions about cognitive neurodynamics of lexical concept activations are drawn. Changing neural accommodation parameters has very strong influence on the dwell time of the trajectories. This may be linked to attention deficits disorders observed in autism in case of strong enslavement, and to ADHD-like behavior in case of weak enslavement.
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
Abstract. Analysis of dynamics of biologically motivated neural networks allows for studying non-linear processes responsible for cognitive functions and thus provides adequate language to understand complex mental processes, including psychiatric syndromes and disorders. Problems with attention shifts that are at the roots of Autism Spectrum Disorders (ASD) and AttentionDeficit/Hyperactivity Disorder (ADHD), have been investigated using network model of Posner Visual Orienting Task (PVOT). Changing parameters that control biophysical properties of model neurons and cause network dysfunctions provides plausible explanations of many strange ASD and ADHD phenomena.
The article describes a study aimed at developing an interaction template for mobile card games for visually impaired gamers. First, accessibility features of existing mobile card games were analyzed. Then various types of actions in common card games were studied and classified to proper categories. Next a simplified layout was proposed in a simplified form of single card view. The interaction mode also was limited to six simple gestures. This approach was used in the sample game. Finally, the new approach was evaluated obtaining satisfactory results.
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