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J4 ›› 2014, Vol. 11 ›› Issue (1): 151-157.doi: 10.1016/S1672-6529(14)60030-5

• article • Previous Articles    

Bio-Inspired Electromagnetic Protection Based on Neural Information Processing

Xiaolong Chang, Shanghe Liu, Menghua Man, Weihua Han, Jie Chu, Liang Yuan   

  1. 1. Institute of Electrostatic and Electromagnetic Protection, Mechanical Engineering College, Shijiazhuang 050003, P. R. China
    2. Institute of Semiconductors, Chinese Academy of Science, Beijing 100083, P. R. China
    3. Department of Information Engineering, Mechanical Engineering College, Shijiazhuang 050003, P. R. China
  • Received:2013-03-16 Revised:2013-12-16 Online:2014-01-10 Published:2013-01-10
  • Contact: Xiaolong Chang E-mail:longsmalldragon@163.com
  • About author:Xiaolong Chang, Shanghe Liu, Menghua Man, Weihua Han, Jie Chu, Liang Yuan

Abstract:

Electronic systems are vulnerable in electromagnetic interference environment. Although many solutions are adopted to solve this problem, for example shielding, filtering and grounding, noise is still introduced into the circuit inevitably. What impresses us is the biological nervous system with a vital property of robustness in noisy environment. Some mechanisms, such as neuron population coding, degeneracy and parallel distributed processing, are believed to partly explain how the nervous system counters the noise and component failure. This paper proposes a novel concept of bio-inspired electromagnetic protec-tion making reference to the characteristic of neural information processing. A bionic model is presented here to mimic neuron populations to transform the input signal into neural pulse signal. In the proposed model, neuron provides a dynamic feedback to the adjacent one according to the concept of synaptic plasticity. A simple neural circuitry is designed to verify the rationality of the bio-inspired model for electromagnetic protection. The experiment results display that bio-inspired electromagnetic pro-tection model has more power to counter the interference and component failure.

Key words: biological nervous system, robustness, population coding, bio-inspired electromagnetic protection model, neural circuitry