不同重连概率的小世界脉冲神经网络抗扰功能研究

现今各种电磁干扰对电子系统造成的不良影响越来越严重,传统防护方式的局限性日益凸显。电磁仿生学由此被提出,目的是通过借鉴生物体的自适应抗扰的优良特性,以期建立新的防护模式。构建了以Izhikevich神经元模型为节点,兴奋性和抑制性突触可塑性模型共同调节基于小世界网络拓扑的小世界脉冲神经网络;基于复杂网络理论对比分析了不同重连概率的小世界网络的拓扑特性;对比分析了不同重连概率的小世界脉冲神经网络在高斯白噪声刺激下的抗扰功能。实验结果表明:小世界网络的平均路径长度和全局效率值受重连概率的影响较小,平均聚类系数和小世界属性受重连概率的影响较大;构建的不同重连概率的脉冲神经网络均具有一定抗扰功能且高聚类系数和低平均路径长度显著的小世界脉冲神经网络抗扰功能最优。

Anti-interference ability of small world spiking neural networks with different rewiring probabilities

Nowadays, the adverse effects of various electromagnetic interferences on electronic systems are becoming more serious today, and the shortcomings of traditional anti-electromagnetic interfe- rence methods are increasingly prominent. Electromagnetic bionics proposes to establish a new protection mode based on the bionic model by referring to the excellent characteristics of adaptive anti-interference of organisms. The small-world spiking neural network with the Izhikevich model and the plasticity of excitatory and inhibitory is constructed. Based on complex network theory, topology characteristics of small world networks with different rewiring probability are compared. The anti-interference ability of small world spiking neural networks with different rewiring probability under Gaussian white noise are compared. The experimental results show that average path length and global efficiency of the small world network are less affected by the rewiring probability, and average clustering coefficient and small world property are greatly affected by the rewiring probability. The small world spiking neural network has certain anti-interference ability, and the network with high clustering coefficient and low average path length has the best anti-interference ability.