Autonomic learning via saturation gain method, and synchronization between neurons | |
Liu, Zhilong1; Zhou, Ping2; Ma, Jun1,2,3,4; Hobiny, Aatef3; Alzahrani, Faris3 | |
2020-02 | |
发表期刊 | Chaos, Solitons and Fractals |
ISSN | 09600779 |
卷号 | 131 |
摘要 | Synchronization provides an effective way for stable signal exchange and balance in membrane potentials of neurons. Both electric synapse and chemical synapse play important role in processing signals by emitting signal and receiving signals, and the encoded signals are estimated by a variety of synaptic currents. For two or more neurons, the synaptic current can pass along the coupling channels with feasible self-adaption and then synaptic plasticity is formed. The occurrence of synaptic currents generates complex biophysical effect because continuous propagation and pumping of calcium, sodium and potassium can induce time-varying physical field intra- and extracellular of cell. Indeed, the field effect becomes more distinct when more neurons are involved in a functional region of the nervous system. To decrease the energy consumption and obtain fast signal exchange, autonomic learning is often activated to select the most appropriate coupling gain in the synapses connected to neurons. That is, synapse can increase the synaptic intensity carefully before reaching synchronization. In this paper, the two-variable Fitzhugh-Nagumo neuron driven by voltage source is used to investigate the synchronization stability when hybrid synapse is applied between two neurons. By using the saturation gain method, the synapse intensity is increased with appropriate step until synchronization is reached, and then the coupling intensity is fixed to find the threshold for stabilizing complete synchronization. It gives new clues to understand the synaptic plasticity from physical viewpoint. © 2019 Elsevier Ltd |
关键词 | Energy utilization Neurons Autonomic learning Complete synchronization Field coupling Fitzhugh Nagumo neurons Hybrid synapse Membrane potentials Synaptic plasticity Synchronization stability |
DOI | 10.1016/j.chaos.2019.109533 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
WOS研究方向 | Mathematics ; Physics |
WOS类目 | Mathematics, Interdisciplinary Applications ; Physics, Multidisciplinary ; Physics, Mathematical |
WOS记录号 | WOS:000514552900007 |
出版者 | Elsevier Ltd |
EI入藏号 | 20194907788660 |
EI主题词 | Synchronization |
EI分类号 | 461.9 Biology - 525.3 Energy Utilization - 961 Systems Science |
来源库 | Compendex |
分类代码 | 461.9 Biology - 525.3 Energy Utilization - 961 Systems Science |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/115820 |
专题 | 理学院 |
通讯作者 | Ma, Jun |
作者单位 | 1.Lanzhou Univ Technol, Dept Phys, Lanzhou 730050, Peoples R China; 2.Chongqing Univ Posts & Telecommun, Sch Sci, Chongqing 430065, Peoples R China; 3.King Abdulaziz Univ, Dept Math, POB 80203, Jeddah 21589, Saudi Arabia; 4.Univ Elect Sci & Technol, Sch Phys, Chengdu 610054, Peoples R China |
第一作者单位 | 理学院 |
通讯作者单位 | 理学院 |
第一作者的第一单位 | 理学院 |
推荐引用方式 GB/T 7714 | Liu, Zhilong,Zhou, Ping,Ma, Jun,et al. Autonomic learning via saturation gain method, and synchronization between neurons[J]. Chaos, Solitons and Fractals,2020,131. |
APA | Liu, Zhilong,Zhou, Ping,Ma, Jun,Hobiny, Aatef,&Alzahrani, Faris.(2020).Autonomic learning via saturation gain method, and synchronization between neurons.Chaos, Solitons and Fractals,131. |
MLA | Liu, Zhilong,et al."Autonomic learning via saturation gain method, and synchronization between neurons".Chaos, Solitons and Fractals 131(2020). |
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