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.
Cite this article
Anti-interference ability of deep spiking neural network
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
*Correspondence: email@example.com (Lei Guo)
J. Integr. Neurosci. 2018, 17(4), 307–311; https://doi.org/10.31083/j.jin.2018.04.0407
Submitted: 15 December 2018 | Accepted: 7 January 2018 | Published: 15 November 2018
Copyright: © 2018 The authors. Published by IMR press.
This is an open access article under the CC BY–NC 4.0 license. https://creativecommons.org/licenses/by-nc/4.0/
Deep spiking neural network