School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, People's Republic of China.
Chaos. 2013 Mar;23(1):013128. doi: 10.1063/1.4790829.
The effects of time delay on stochastic resonance in small-world neuronal networks are investigated. Without delay, an intermediate intensity of additive noise is able to optimize the temporal response of the neural system to the subthreshold periodic signal imposed on all neurons constituting the network. The time delay in the coupling process can either enhance or destroy stochastic resonance of neuronal activity in the small-world network. In particular, appropriately tuned delays can induce multiple stochastic resonances, which appear intermittently at integer multiples of the oscillation period of weak external forcing. It is found that the delay-induced multiple stochastic resonances are most efficient when the forcing frequency is close to the global-resonance frequency of each individual neuron. Furthermore, the impact of time delay on stochastic resonance is largely independent of the small-world topology, except for resonance peaks. Considering that information transmission delays are inevitable in intra- and inter-neuronal communication, the presented results could have important implications for the weak signal detection and information propagation in neural systems.
研究了时滞对小世界神经元网络中随机共振的影响。在无延迟的情况下，适中强度的加性噪声能够优化神经网络对施加于构成网络的所有神经元的亚阈值周期信号的时间响应。耦合过程中的时滞会增强或破坏小世界网络中神经元活动的随机共振。特别是，适当调整的延迟可以诱导多个随机共振，这些共振在弱外部强迫的振荡周期的整数倍处间歇性出现。研究发现，当外部强迫频率接近每个神经元的全局共振频率时，延迟诱导的多个随机共振效率最高。此外，除了共振峰之外，时滞对随机共振的影响在很大程度上与小世界拓扑结构无关。考虑到神经元内和神经元间信息传输延迟是不可避免的，因此，所提出的结果对于神经系统中的弱信号检测和信息传播可能具有重要意义。
