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

Jia Zhao, Ying Mei Qin, Yan Qiu Che

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number122030
JournalPhysica A: Statistical Mechanics and its Applications
Volume533
DOIs
StatePublished - Nov 1 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Condensed Matter Physics

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