Detection of bursts in neuronal spike trains by the mean inter-spike interval method  

作　　者：Lin Chen Yong Deng Weihua Luo Zhen Wang Shaoqun Zeng 

机构地区：[1]Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan 430074, China [2]College of Mechanical Engineering, Guangxi University, Nanning, 530004, China [3]College of Optoelectronic Science of Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

出　　处：《Progress in Natural Science:Materials International》2009年第2期229-235,共7页自然科学进展·国际材料（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant No.30727002);National High-Tech Research and Development Program of China (2006AA02Z343)

摘　　要：Bursts are electrical spikes firing with a high frequency, which are the most important property in synaptic plasticity and information processing in the central nervous system. However, bursts are difficult to identify because bursting activities or patterns vary with phys- iological conditions or external stimuli. In this paper, a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter （mean inter-spike interval） according to intrinsic properties of the detected burst spike trains, without any arbitrary choices or any operator judgment. When the mean value of several successive inter-spike intervals is not larger than the parameter, a burst is identified. By this method, bursts can be automatically extracted from different bursting patterns of cul.tured neurons on multi-electrode arrays, as accurately as by visual inspection. Furthermore, significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations. 2008 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.Bursts are electrical spikes firing with a high frequency,which are the most important property in synaptic plasticity and information processing in the central nervous system. However,bursts are difficult to identify because bursting activities or patterns vary with phys-iological conditions or external stimuli. In this paper,a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter (mean inter-spike interval) according to intrinsic properties of the detected burst spike trains,without any arbitrary choices or any operator judgment. When the mean value of several successive inter-spike intervals is not larger than the parameter,a burst is identified. By this method,bursts can be automatically extracted from different bursting patterns of cultured neurons on multi-electrode arrays,as accurately as by visual inspection. Furthermore,significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations.

关 键 词：Inter-spike interval BURSTS Spike trains Multi-electrode arrays 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]