Effects of sine-Wiener noise on signal propagation in a randomly connected neural network
Effects of sine-Wiener noise on signal propagation in a randomly connected neural network
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正弦维纳噪声对随机连接神经网络中信号传播的影响
DOI：
10.1016/j.physa.2019.122030
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发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
Yan-Qiu Che
中科院分区：
文献类型：
--
作者：
Jia Zhao;Ying-Mei Qin;Yan-Qiu Che
在一个基于Izhikevich神经元模型的随机连接神经网络中，研究了sine-Wiener（SW）噪声对信号传播的影响.该网络中突触的轴突传导延迟、神经元之间的连接概率以及兴奋性神经元和抑制性神经元之间的比例与哺乳动物大脑皮层相似.研究发现，短波噪声对弱信号在网络中的传播有增强作用。除了短波噪声参数外，网络的特征参数对信号的传播也起着重要的作用。此外，还发现神经网络存在一个敏感频率，当信号频率接近网络敏感频率时，网络对弱信号的增强效果最佳。总之，本文的结果表明，适当的自相关时间和强度的SW噪声可以促进弱信号在随机连接的神经网络中的传播。
We investigate the effects of sine-Wiener (SW)-noise on signal propagation in a randomly connected neural network based on Izhikevich neuron model in detail, in which the axonal conduction delays of synapses, the linkage probability between neurons and the ratio between excitatory and inhibitory neurons of the network are set similarly with the mammalian neocortex. It is found that the SW-noise can enhance the propagation of weak signal in the network. Besides the parameters of SW-noise, the characteristic parameters of the network also play important roles in signal propagation. Furthermore, it is found that the neural network has its sensitive frequency that can optimally enhance the propagation of weak signal when the signal’s frequency is close to the network’s sensitive frequency. In summary, the results here suggest that the SW-noise with suitable self-correlation time and intensity can facilitate the propagation of weak signal in the randomly connected neural network.
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