Open AccessThe second super-harmonic stochastic resonance in the neural networks with small-world character
Author(s) -
X. R. Zhou,
Luo Xiao-Shu,
Pinqun Jiang,
Weihong Yuan
Publication year - 2007
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.56.5679
Subject(s) - character (mathematics) , stochastic resonance , artificial neural network , nonlinear system , resonance (particle physics) , amplitude , signal (programming language) , harmonic , physics , coupling (piping) , noise (video) , computer science , statistical physics , mathematics , acoustics , quantum mechanics , artificial intelligence , materials science , geometry , image (mathematics) , programming language , metallurgy
Stochastic resonance is a common natural phenomenon in nonlinear systems. By studying the relations between the out put signal-to-noise ratio (SNR) of the biologic neural network with small-world character and the rewiring probability p which reflects the effect of small-world,the coupling strength c, amplitude A of input signal, we revealed some regularities of the second super-harmonic stochastic resonance in the biologic neural network, and found that the out put SNR doesn't monotonicly increase as the forcing amplitude A increases, but there exists an optimal value AO for the Hodgkin-Huxley (HH) neural network with small-world character. The out put SNR reaches its maximum when A is equal to AO.