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

In this paper, adaptive stochastic resonance in time-delayed Newman-Watts small-world neuronal networks is studied, where the strength of synaptic connections between neurons is adaptively modulated by spike-timing-dependent plasticity (STDP). Numerical results show that, in the absence of information transmission delay, the phenomenon of stochastic resonance occurs and the efficiency of networked stochastic resonance can be slightly depressed by STDP. Due to the reduction of strong couplings induced by STDP, for a larger adjusting rate of STDP, a smaller peak value of the resonance response is obtained. In addition, the effect of stochastic resonance can be either promoted or destroyed by time delay, and multiple stochastic resonances appear intermittently at the integer multiples of periods of the subthreshold forcing. Furthermore, it is demonstrated that the networked stochastic resonance can also be dramatically affected by the small-world topology. For small and moderate adjusting rate of STDP, fine-tuning of the probability of adding links can significantly enhance the effect of stochastic resonance in adaptive neural network. Additionally, there is an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks and the location of this span depends largely on the time delay and adjusting rate.