Understanding neurodynamical systems via Fuzzy Symbolic Dynamics
نویسندگان
چکیده
منابع مشابه
Understanding neurodynamical systems via Fuzzy Symbolic Dynamics
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, b...
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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 concentration of radioactive traces (PET) in different parts of the brain. Various basis set decomposition techniques that try to discover components that carry meaningful in...
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◦ Geodesic flows on compact manifolds with negative sectional curvature. Later, we relax uniform hyperbolicity to an asymptotic one, called non-uniform hyperbolicity. Two examples of such systems are: ◦ Slow down of fA : T → T, see [9]. ◦ Geodesic flows on surfaces with nonpositive curvature. Introductory example: Smale’s horseshoe [21]. Let g : K → K be Smale’s horseshoe map, and σ : Σ→ Σ the ...
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ژورنال
عنوان ژورنال: Neural Networks
سال: 2010
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2009.12.005