神经元网络同步放电的抗扰特性

为了揭示神经元网络在噪声环境下实现可靠信息处理的内在机制,利用神经元模型对噪声环境下神经元网络同步放电的抗扰特性进行数值计算和分析.给出了定量描述神经元网络放电同步程度和抗扰特性的评价指标,并研究了放电同步程度和抗扰特性间的内在联系.数值仿真结果表明,神经元数目和耦合强度对网络的同步和抗扰特性影响较大;在一定范围内,神经元放电同步程度与抗扰特性强弱间具有近似线性的内在联系.因此,神经元网络可以利用同步放电机制抑制噪声干扰,执行可靠的信息编码与处理.

Anti-Interference of Neuronal Network Synchronization

Abstract： To understand the underlying mechanism of reliable neural information processing in noise, the neuronal model was adopted to build neuronal network and its activities of synchronized firing and characteristics of anti inference were analyzed. The degree of synchronization and ability of anti interference were quantified and the relation between them was discussed. The simulation results show that the scales of neuronal network and strength of coupling have remarkable effects both on synchronization and anti interference; under some conditions, the ability of anti inference is almost linear with the degree of neuronal synchronization. Therefore, it is concluded that neuronal synchronization can reduce noise and execute reliable information coding and processing in the brain.