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The Research on the Stochastic Resonance Based of Feedback FitzHugh-Nagumo Neural Network

机译:基于反馈Fitzhugh-Nagumo神经网络的随机共振研究

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The research on stochastic resonance phenomenon of neuron had shown the important theoretical significance and application value of the weak signal detection. The robustness performed not very well during the process of the weak signal detection, which based on the stochastic resonance of the traditional FitzHugh-Nagumo (FHN) neuron model. The addition of feedback loop which achieved the reaction formation from the model response to the input layer, could improve the performance of the weak signal detection. Comparative analyses of the traditional and improved FHN neural network were taken by combining with the spike frequency and amplitude. -The results show that the responses of stochastic resonance based on feedback FHN neural network possess better performance and stability during a certain range of noise intensity. Thus, the stochastic resonance of this improved feedback FHN network can be more perfectly applied to the weak signal detection and transmission.
机译:神经元随机共振现象研究表明了弱信号检测的重要理论意义和应用价值。在弱信号检测过程中,鲁棒性在基于传统Fitzhugh-nagumo(FHN)神经元模型的随机共振期间的过程中的不太良好。添加从模型响应到输入层的反应形成的反馈回路可以提高弱信号检测的性能。通过与尖峰频率和幅度组合来拍摄传统和改进的FHN神经网络的比较分析。结果表明,基于反馈FHN神经网络的随机共振的响应在一定程度的噪声强度期间具有更好的性能和稳定性。因此,这种改进的反馈FHN网络的随机共振可以更完全地应用于弱信号检测和传输。

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