摘要
为研究神经元的放电时间序列随时间的演化特性,提出了一种将放电时间序列的时间域映射到网络域进行处理的方法,即研究基于神经元的复杂网络随时间的演化特征来刻画神经元放电时间序列的时变特性。通过构建滑动时间窗内复杂网络拓扑,并计算其局部可视图的统计特性来实现时间序列时变特征的描述。对神经元map模型三种簇放电时间序列进行复杂网络构建并实现网络拓扑可视化,同时分析网络的统计特性来验证方法的有效性。结果表明,网络的拓扑、平均路径长度和聚类系数均能反映原时间序列的时变形态特征,并对神经元簇放电具有参数敏感性;簇放电稀疏程度与社团大小存在相关性。神经元放电时间序列网络域的时变演化特征能刻画其时间域特性,为神经电信号的处理提供了新的思路。
In order to investigate the evaluation of neuronal firing time series with time,this paper proposed an improved visibility graph method called local visibility graph method for constructing complex networks from time series,which could map time-domain of neuronal firing time series into network domain. By constructing a complex network topology and calculating its statistical properties in a sliding time window,the method described the time-varying statistical characteristics of neuronal firing time series. As a paradigm,three time series of different bursting modes generated by map neuronal model were used to construct networks,realize visualize topology and analyze statistical characteristics. The results show that network topology,ave-rage path length and clustering coefficient can depict time-varying morphological characteristics of the time series. The three properties show parameter sensitivity to neuronal bursting. There is a correlation between sparse degree of neuronal bursting and the community size in the complex networks. Evolution characteristics of neuronal firing time series in network-domain can depict their time-domain features,and the ideology proposed in this paper provides a new way for on-line neural signal processing.
出处
《计算机应用研究》
CSCD
北大核心
2014年第12期3756-3758,3783,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61072012)
河北科技大学博士科研启动基金资助项目(QD201302)
关键词
局部可视图
复杂网络
神经元
混沌放电
local visibility graph
complex networks
neuron
chaotic firing
