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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (4): 307-311    DOI: 10.31083/j.jin.2018.04.0407
Research article | Next articles
Anti-interference ability of deep spiking neural network
Lei Guo1, 2, 3, *(), Hongyi Shi1, 2, 3, Yunge Chen1, 2, 3, Hongli Yu1, 2, 3
1 Department of Biomedical Engineering, College of Electrical Engineering, Hebei University of Technology, 300130, Tianjin, China
2 State Key Laboratory of Reliable and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, 300130, Tianjin, China
3 Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, 300130, Tianjin, China
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Organisms have the advantages of self-adaptive mechanisms and an anti-interference ability. To investigate the anti-interference ability of a deep spiking neural network that simulates a biological neural system, the correlation between membrane potential and firing rate is interpreted as an anti-interference index so as to investigate the anti-interference ability of a deep spiking neural network under the regulation of synaptic plasticity in the presence of different amplitudes of an electric field. When the relative variation rate of firing rate is less than 10% or the correlation between the membrane potential is greater than half, the influence of electric field on neural network is relatively small. Otherwise, the influence is relatively large. Simulation results show that: based on the regulation of synaptic plasticity, within a certain electric field interference range, the relative rate of variation of cell firing rates is small compared with non-interference, while correlation between the membrane potential in each layer is large when compared to non-interference.

Key words:  Deep spiking neural network      synaptic plasticity      anti-interference      electric field      firing rate      correlation     
Submitted:  15 December 2018      Accepted:  07 January 2018      Published:  15 November 2018     
*Corresponding Author(s):  Lei Guo     E-mail:

Cite this article: 

Lei Guo, Hongyi Shi, Yunge Chen, Hongli Yu. Anti-interference ability of deep spiking neural network. Journal of Integrative Neuroscience, 2018, 17(4): 307-311.

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Figure.1.  Pulse time sequence diagrams.

Figure.2.  The firing rate of each layer.

Fig. 3.  Firing rate of output layer neuron.

Fig. 4.  The relative variation rate of firing rate.

Fig. 5.  Correlation between membrane potential of each layer.

Fig. 6.  Correlation between the membrane potential of the output layer neuron and other layers.

Table 1  Correspondences between membrane potential correlations and electric field amplitude
range of electric field amplitude 1~4 5~9 10~13 14~20
range of correlation 0.61~0.71 0.45~0.67 0.26~0.36 0.14~0.24
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