Effects of time delay on the stochastic resonance in small-world neuronal networks

2013 ◽  
Vol 23 (1) ◽  
pp. 013128 ◽  
Author(s):  
Haitao Yu ◽  
Jiang Wang ◽  
Jiwei Du ◽  
Bin Deng ◽  
Xile Wei ◽  
...  
Keyword(s):  
Time Delay ◽  
Stochastic Resonance ◽  
Neuronal Networks ◽  
Small World

Adaptive stochastic resonance in self-organized small-world neuronal networks with time delay

2015 ◽  
Vol 29 (1-3) ◽  
pp. 346-358 ◽  
Author(s):  
Haitao Yu ◽  
Xinmeng Guo ◽  
Jiang Wang ◽  
Chen Liu ◽  
Bin Deng ◽  
...  
Keyword(s):  
Time Delay ◽  
Stochastic Resonance ◽  
Neuronal Networks ◽  
Small World ◽  
Self Organized

Effects of time delay and random rewiring on the stochastic resonance in excitable small-world neuronal networks

2013 ◽  
Vol 87 (5) ◽  
Author(s):  
Haitao Yu ◽  
Jiang Wang ◽  
Jiwei Du ◽  
Bin Deng ◽  
Xile Wei ◽  
...  
Keyword(s):  
Time Delay ◽  
Stochastic Resonance ◽  
Neuronal Networks ◽  
Small World

How electromagnetic induction and coupled delay affect stochastic resonance in a modified neuronal network subject to phase noise

2019 ◽  
Vol 33 (26) ◽  
pp. 1950302
Author(s):  
Xiao Li Yang ◽  
Xiao Qiang Liu
Keyword(s):  
Time Delay ◽  
Phase Noise ◽  
Stochastic Resonance ◽  
Neuronal Network ◽  
Electromagnetic Induction ◽  
Small World ◽  
Feedback Gain ◽  
Small World Network ◽  
Induced Current ◽  
Resonance Response

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.

Stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses

2014 ◽  
Vol 60 ◽  
pp. 40-48 ◽  
Author(s):  
Jiang Wang ◽  
Xinmeng Guo ◽  
Haitao Yu ◽  
Chen Liu ◽  
Bin Deng ◽  
...  
Keyword(s):  
Stochastic Resonance ◽  
Neuronal Networks ◽  
Small World ◽  
Chemical Synapses

Impact of Partial Time Delay on Temporal Dynamics of Watts–Strogatz Small-World Neuronal Networks

2017 ◽  
Vol 27 (07) ◽  
pp. 1750112 ◽  
Author(s):  
Hao Yan ◽  
Xiaojuan Sun
Keyword(s):  
Time Delay ◽  
Firing Rate ◽  
Neuronal Networks ◽  
Temporal Dynamics ◽  
Great Influence ◽  
Small World ◽  
Temporal Coherence ◽  
Small Time ◽  
Neuronal Firing ◽  
Two Parameters

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts–Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay [Formula: see text] and the other is the probability of partial time delay [Formula: see text]. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay [Formula: see text], the probability [Formula: see text] could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay [Formula: see text], temporal coherence and mean firing rate do not have great changes with respect to [Formula: see text]. Time delay [Formula: see text] always has great influence on both temporal coherence and mean firing rate no matter what is the value of [Formula: see text]. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay [Formula: see text]. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

Sign in / Sign up

Export Citation Format

Share Document