TY - JOUR

T1 - Effects of sine-Wiener noise on signal propagation in a randomly connected neural network

AU - Zhao, Jia

AU - Qin, Ying Mei

AU - Che, Yan Qiu

N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (Grant No. 31571111 , 81600793 and 61401312 ) and the Natural Science Foundation of Tianjin, China (Grant No. 17JCQNJC03700 ).
Publisher Copyright:
© 2019 Elsevier B.V.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - 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.

AB - 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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U2 - 10.1016/j.physa.2019.122030

DO - 10.1016/j.physa.2019.122030

M3 - Article

AN - SCOPUS:85069603602

VL - 533

JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

SN - 0378-4371

M1 - 122030

ER -