Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise.

Lei Guo,Enyu Kan,Guizhi Xu,Youxi Wu,Huan Lv,Jun Ma

doi:10.1371/journal.pone.0244683

Lei Guo, Enyu Kan + Show 4 more

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https://doi.org/10.1371/journal.pone.0244683

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Journal: PloS one	Publication Date: Dec 31, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: Hebei University of Technology

Abstract

With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

Highlights

With the development of science and technology, the automation and informatization of human society have been continuously improved, which makes the electromagnetic environment become increasingly complex
The study of the noise suppression ability of the spiking neural network (SNN) based on synaptic plasticity is still in the stage of exploration
We evaluate the noise suppression ability of the scale-free spiking neural network (SFSNN) from different angles and get the consistent experiment result that the SFSNN has a certain degree of noise suppression ability under white Gaussian noise

Summary

Introduction

With the development of science and technology, the automation and informatization of human society have been continuously improved, which makes the electromagnetic environment become increasingly complex. Noise suppression ability and its mechanism analysis of scale-free spiking neural network system has the self-adaptive advantages under the regulation of the nervous system, such as self-learning, self-organizing and self-repairing [5]. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system [6]. The spiking neural network (SNN) is the most biologically interpreted ANN, which can simulate the information processing of the biological brain network by establishing the nonlinear state dynamics behavior of neurons and the regulation process of synaptic weight dynamics [7, 8]. SNN can be widely applied in robot control [12], brain-like research [13, 14], pattern recognition [15] and other fields

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