Influence of the Gaussian colored noise and electromagnetic radiation on the propagation of subthreshold signals in feedforward neural networks

• Published in: Science China. Technological sciences 2021-04, Vol.64 (4), p.847-857
• Main Authors: Ge, MengYan, Wang, GuoWei, Jia, Ya
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Electromagnetic radiation; Engineering; Feedback; Iterative methods; Machine learning; Multilayers; Nervous system; Neural networks; Noise; Noise propagation; Physiology; Propagation; Synapses
• ISSN: 1674-7321; 1869-1900
• DOI: 10.1007/s11431-020-1696-8

Iterative methods are used to simulate the in vitro feedforward neural networks in physiological experiments. Emissivity can be propagated to a minimum of ten groups. However, the discharge activity of each group will be more synchronized. The feedforward neural networks have a wide range of applications in machine learning, and the weight of synapses considerably influences the propagation of weak signals. Herein, we investigated the effect of Gaussian colored noise and electromagnetic radiation on the propagation of the subthreshold excitatory postsynaptic current signals in the input layer of the multilayer Izhikevich neural feedforward networks. In the absence of electromagnetic radiation, the excitatory postsynaptic current signal is stably propagated and amplified in multilayer feedforward neural networks under the optimal Gaussian colored noise strength or correlation time in the output layer of the network. Compared with the case in which there is no electromagnetic radiation, the presence of electromagnetic radiation slightly reduces the propagation of weak signals. Further, the time required to propagate the excitatory postsynaptic current signal to the output layer increases with the increasing feedback gain. The feedforward neural network considered in this study is a considerably simple model. More complex structures, such as backward connection and delayed feedback, can be observed in real biological systems. Hence, the next step will be to study more complex neural models with neuron models based on the physiological experimental data and compare them with real biological systems. Furthermore, the study of neural networks can be combined with an experimental study about the auditory nervous system of bats to understand the biological mechanism associated with the auditory system function of bats from two perspectives.