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- W2898872435 abstract "In this work, we use the Discrete General Beta Distribution (DGBD) to characterize stochastic resonance (SR). We present an SR model that uses the balance of energy (gain versus losses) between two signals, namely, a weak harmonic signal plus noise at different levels. The energy balance is done in two different states (representing below and above sensitivity threshold). The states are characterized by two wells divided by a barrier which are mathematically described by an asymmetrical Duffing potential. In order to have transitions between states, the presence of noise is necessary. With this model, the activity of neurons below and above the sensitivity threshold states is simulated and the time series is generated. Fourier analysis of these time series results in the well-known inverse-U-shaped behavior between signal and noise that characterizes SR. In the second part of the work, we ranked the values from the time series, simulated with the SR model, in descendent order from high to low values and then we calculate the fitting parameters for the DGBD for each noise condition and show that SR could also be characterized by DGBD parameters." @default.
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- W2898872435 date "2018-10-31" @default.
- W2898872435 modified "2023-09-30" @default.
- W2898872435 title "Characterization of Stochastic Resonance by the Discrete General Beta Distribution" @default.
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- W2898872435 doi "https://doi.org/10.1007/978-981-13-1642-5_65" @default.
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