| Noise-Assisted Signal Reception in Threshold Neuron |
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| 作者姓名： | WANG You-guo WU Le-nan ZHANG Dan.School of Mathematics and Physics Nanjing University of Posts and Telecommunications Nanjing P. R. China . |
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| 作者单位： | WANG You-guo 1，2，WU Le-nan2，ZHANG Dan31.School of Mathematics and Physics，Nanjing University of Posts and Telecommunications，Nanjing 210003，P. R. China;2.Department of Radio Engineering，Southeast University，Nanjing 210096，P. R. China; 3.School of Telecommuncation and Information Engineering，Najing University of Posts and Telecommunications，Nanjing 210003，P.R. China |
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| 摘 要： | 1IntroductionSometi mes,noise can i mprove signal transmission orsignal processing in a nonlinear system.This phe-nomenonis the so-called Stochastic Resonance(SR)1].Most studies of SRinvolve asubthresholdsignal througha single nonlinear systemand the non…
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| 收稿时间： | 4 April 2005 |
Noise-Assisted Signal Reception in Threshold Neuron |
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| WANG You-guo ,,WU Le-nan,ZHANG Dan.School of Mathematics and Physics,Nanjing University of Posts and Telecommunications,Nanjing ,P. R. China,..Noise-Assisted Signal Reception in Threshold Neuron[J].The Journal of China Universities of Posts and Telecommunications,2006,13(1):79-83. |
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| Authors: | WANG You-guo WU Le-nan ZHANG Dan |
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| Affiliation: | 1. School of Mathematics and Physics, Nanjing University of Posts and Telecommunications, Nanjing 210003, P. R. China;2. Department of Radio Engineering, Southeast University, Nanjing 210096, P. R. China;3. School of Telecommuncation and Information Engineering, Najing University of Posts and Telecommunications, Nanjing 210003, P.R. China |
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| Abstract: | In certain cases, noises can improve signal transmission or signal processing. This phenomenon is the so-called stochastic resonance. In this paper, we firstly present two theorems to prove that the noisy threshold neuron shows stochastic resonance in terms of the probability of correct reception. Secondly, we analytically discuss stochastic resonance effects and give the probability-optimal noise levels for four representative noises. Finally, we discuss the stochastic gradient ascent learning law, which can be used to find the probability-optimal noise levels. We also present our simulation results for the four representative noises. These results indicate that stochastic resonance is favorable both in biological neurons and in signal processing. |
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| Keywords: | stochastec resonance threshold neuron probability of correct reception |
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