| 满梦华,蔡娜,马贵蕾,王震.模仿神经元网络抗扰特性的电磁防护仿生研究[J].装备环境工程,2017,14(4):9-15. MAN Meng-hua,CAI Na,MA Gui-lei,WANG Zhen.Study on Electromagnetic Protection Bionics by Mimicking the Anti-interference Me-chanism of Neural network[J].Equipment Environmental Engineering,2017,14(4):9-15. |
| 模仿神经元网络抗扰特性的电磁防护仿生研究 |
| Study on Electromagnetic Protection Bionics by Mimicking the Anti-interference Me-chanism of Neural network |
| 投稿时间：2016-10-31 修订日期：2017-04-15 |
| DOI：10.7643/ issn.1672-9242.2017.04.003 |
| 中文关键词: S空间编码 Hodgkin-Huxley模型 噪声 抗扰 |
| 英文关键词:S-space coding theory Hodgkin-Huxley model noise anti-inference |
| 基金项目:国家自然科学基金项目（51407194） |
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| Author | Institution |
| MAN Meng-hua | Electrostatic & Electromagnetic Protection Institute, Ordnance Engineering College, Shijiazhuang 050003, China |
| CAI Na | Ordnance Technical Research Institute, Shijiazhuang 050000, China |
| MA Gui-lei | Electrostatic & Electromagnetic Protection Institute, Ordnance Engineering College, Shijiazhuang 050003, China |
| WANG Zhen | Langfang Institute of Health Education, Langfang 065000, China |
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| 中文摘要: |
| 目的 研究神经元在噪声干扰环境下信息处理的抗扰特性，为电磁防护仿生研究提供有益借鉴。方法 利用Hodgkin-Huxley模型建模神经元电信号的产生，结合S空间编码理论分析神经信息的表达。在此基础之上，研究神经信息处理在噪声干扰环境下的抗扰特性。建立具有噪声耦合方式的神经元数学模型，并在不同噪声强度下，计算神经元输出电信号对输入刺激的S空间编码，讨论噪声对编码的影响。结果 在S空间中，神经元将输入刺激信号编码成符号序列，符号序列间的排序关系与输入信号频率间的排序关系所对应。输入噪声能够改变符号序列的值，但并没有改变符号序列间的排序关系，从而不会影响神经元在S空间中所表达的信息。结论 S空间编码是神经元抵御输入噪声干扰的一种重要机制，值得电子系统借鉴，以提高其抗扰能力。 |
| 英文摘要: |
| Objective To study the good anti-interference ability of neural system of organism appears during information process, which can bring enlightenment to the study of bio-inspired electromagnetic protection. Methods We study the underlying mechanism of neural information processing in noise by using the modified bursting Hodgkin-Huxley neuron model to construct simulation models of neural system and S-space coding theory to analyzing neural information. The neural simulation model with different noise intensity is built, the neural information is coded by S-space coding theory, and influence of noise on neural coding is discussed. Results The results show that the neural information is encoded to symbol sequences in S-space and the frequency of input signal has monotonic relationship with the symbol sequences. The input noise changes the symbols of the symbol sequences but does not change the monotonic relationship, that is, the input noise doesn’t influence the information processing in S-space. Conclusion S-space coding theory is an important mechanism for anti-interference ability of neural system, which is worth to draw lessons from by electronic system. |
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