Stochastic resonance at phase noise in signal transmission

2000 ◽  
Vol 61 (1) ◽  
pp. 940-943 ◽  
Author(s):  
François Chapeau-Blondeau
1997 ◽  
Vol 33 (20) ◽  
pp. 1666 ◽  
Author(s):  
X. Godivier ◽  
J. Rojas-Varela ◽  
F. Chapeau-Blondeau

2005 ◽  
Vol 41 (2) ◽  
pp. 91 ◽  
Author(s):  
C. Loyez ◽  
C. Lethien ◽  
R. Kassi ◽  
J.P. Vilcot ◽  
D. Decoster ◽  
...  

2020 ◽  
Vol 23 (1) ◽  
pp. 44-49 ◽  
Author(s):  
Fabing Duan ◽  
Lingling Duan ◽  
Francois Chapeau-Blondeau ◽  
Yuhao Ren ◽  
Derek Abbott

2019 ◽  
Vol 88 (6) ◽  
pp. 063001 ◽  
Author(s):  
Jong-Hoon Huh ◽  
Yoshimitsu Yano ◽  
Naoto Miyagawa

2019 ◽  
Vol 33 (26) ◽  
pp. 1950302
Author(s):  
Xiao Li Yang ◽  
Xiao Qiang Liu

Through introducing the ingredients of electromagnetic induction and coupled time delay into the original Fitzhugh–Nagumo (FHN) neuronal network, the dynamics of stochastic resonance in a model of modified FHN neuronal network in the environment of phase noise is explored by numerical simulations in this study. On one hand, we demonstrate that the phenomenon of stochastic resonance can appear when the intensity of phase noise is appropriately adjusted, which is further verified to be robust to the edge-added probability of small-world network. Moreover, under the influence of electromagnetic induction, the phase noise-induced resonance response is suppressed, meanwhile, a large noise intensity is required to induce stochastic resonance as the feedback gain of induced current increases. On the other hand, when the coupled time delay is incorporated into this model, the results indicate that the properly tuned time delay can induce multiple stochastic resonances in this neuronal network. However, the phenomenon of multiple stochastic resonances is found to be restrained upon increasing feedback gain of induced current. Surprisingly, by changing the period of phase noise, multiple stochastic resonances can still emerge when the coupled time delay is appropriately tuned to be integer multiples of the period of phase noise.


2002 ◽  
Vol 12 (03) ◽  
pp. 629-633 ◽  
Author(s):  
S. MORFU ◽  
J. C. COMTE ◽  
J. M. BILBAULT ◽  
P. MARQUIÉ

We study the influence of spatiotemporal noise on the propagation of square waves in an electrical dissipative chain of triggers. By numerical simulation, we show that noise plays an active role in improving signal transmission. Using the Signal to Noise Ratio at each cell, we estimate the propagation length. It appears that there is an optimum amount of noise that maximizes this length. This specific case of stochastic resonance shows that noise enhances propagation.


Author(s):  
Gregory Knoll ◽  
Benjamin Lindner

AbstractIt has previously been shown that the encoding of time-dependent signals by feedforward networks (FFNs) of processing units exhibits suprathreshold stochastic resonance (SSR), which is an optimal signal transmission for a finite level of independent, individual stochasticity in the single units. In this study, a recurrent spiking network is simulated to demonstrate that SSR can be also caused by network noise in place of intrinsic noise. The level of autonomously generated fluctuations in the network can be controlled by the strength of synapses, and hence the coding fraction (our measure of information transmission) exhibits a maximum as a function of the synaptic coupling strength. The presence of a coding peak at an optimal coupling strength is robust over a wide range of individual, network, and signal parameters, although the optimal strength and peak magnitude depend on the parameter being varied. We also perform control experiments with an FFN illustrating that the optimized coding fraction is due to the change in noise level and not from other effects entailed when changing the coupling strength. These results also indicate that the non-white (temporally correlated) network noise in general provides an extra boost to encoding performance compared to the FFN driven by intrinsic white noise fluctuations.


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