Understanding neurodynamical systems via Fuzzy Symbolic Dynamics

Understanding neurodynamical systems via Fuzzy Symbolic Dynamics,10.1016/j.neunet.2009.12.005,Neural Networks,Krzysztof Dobosz,Wlodzislaw Duch

Understanding neurodynamical systems via Fuzzy Symbolic Dynamics   (Citations: 1)
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Neurodynamical 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.
Journal: Neural Networks , vol. 23, no. 4, pp. 487-496, 2010
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    • ...Symbolic dynamics that he used to describe this continuity has actually led him back to discrete representation but it can be amended [40]...
    • details of this process we have used visualization based on fuzzy symbolic dynamics [40]...

    Wlodzislaw Duch. Neurodynamics and the mind

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