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Autapse-induced multiple stochastic resonances in a modular neuronal network

机译:在模块化神经网络中自动诱导多个随机共振

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This study investigates the nontrivial effects of autapse on stochastic resonance in a modular neuronal network subjected to bounded noise. The resonance effect of autapse is detected by imposing a self-feedback loop with autaptic strength and autaptic time delay to each constituent neuron. Numerical simulations have demonstrated that bounded noise with the proper level of amplitude can induce stochastic resonance; moreover, the noise induced resonance dynamics can be significantly shaped by the autapse. In detail, for a specific range of autaptic strength, multiple stochastic resonances can be induced when the autaptic time delays are appropriately adjusted. These appropriately adjusted delays are detected to nearly approach integer multiples of the period of the external weak signal when the autaptic strength is very near zero; otherwise, they do not match the period of the external weak signal when the autaptic strength is slightly greater than zero. Surprisingly, in both cases, the differences between arbitrary two adjacent adjusted autaptic delays are always approximately equal to the period of the weak signal. The phenomenon of autaptic delay induced multiple stochastic resonances is further confirmed to be robust against the period of the external weak signal and the intramodule probability of subnetwork. These findings could have important implications for weak signal detection and information propagation in realistic neural systems. Published by AIP Publishing.
机译:本研究研究了自闭断对经受有界噪声的模块化神经元网络中随机共振的非动力影响。通过将自助反馈环施加到每个组成神经元的自触发强度和自拔时间延迟来检测自闭断的共振效应。数值模拟已经证明,具有适当幅度的有界噪声可以诱导随机共振;此外,噪声诱导的谐振动力学可以通过自动捕获而显着形状。详细地,对于特定的自触发强度范围,当适当地调整自动时间延迟时,可以引起多个随机共振。当自触发强度非常接近零时,将这些适当调整的延迟分别检测到几乎接近外部弱信号时段的整数倍数;否则,当自动强度略大于零时,它们与外部弱信号的时段不匹配。令人惊讶的是,在这两种情况下,任意两个相邻的调整后的自动延迟之间的差异总是近似等于弱信号的时段。自动延迟诱导的多个随机共振的现象被进一步证实对外部弱信号的时期和子网的血际内概率稳健。这些发现对于在现实神经系统中的弱信号检测和信息传播中可能具有重要意义。通过AIP发布发布。

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  • 来源
    《Chaos 》 |2017年第8期| 共10页
  • 作者单位

    Shaanxi Normal Univ Coll Math &

    Informat Sci Xian 710062 Shaanxi Peoples R China;

    Shaanxi Normal Univ Coll Math &

    Informat Sci Xian 710062 Shaanxi Peoples R China;

    Northwestern Polytech Univ Dept Appl Math Xian 710072 Shaanxi Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自然科学总论 ;
  • 关键词

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