基于最大锋电位间隔的爆发检测自适应算法

在各种类型的培养神经元网络、哺乳动物中枢神经系统和切片中,都可以观察到爆发。爆发是空间-时间放电模式的重要特征,它由一系列高频率发放的连续动作电位组成,由于在时间尺度上的复杂性,使其辨识和探测存在许多困难。自适应算法利用爆发外部锋电位间隔超过爆发内部锋电位间隔的累加和识别爆发本身。基于该算法原理,以爆发内部最大锋电位间隔参数作为确定爆发的约束条件,改进爆发检测自适应算法。实验结果表明,改进算法可以有效地避免爆发的漏检和错检,较准确地检测出神经元的爆发活动,确定爆发活动的数目和持续时间等,爆发检测的平均准确率为93.8%,比原自适应算法提高了35.3%。

IMPROVED SELF-ADAPTIVE ALGORITHM FOR BURST DETECTION BASED ON THE MAXIMUM INTERSPIKE INTERVAL

Bursts are observed in various neuronal types of mammalian central nervous system and in organotypic slice cultures or in various cultures of neural networks in vitro, which are regarded as an important feature of spatial-temporal spike patterns. A sequence of action potentials (spike train) often has high frequency spike episodes that are generally called bursts. The complexity of bursts on timescale brings out many difficulties for identification and detection. Bursts are detected by self-adaptive algorithm when the inter-burst intervals (inter-spike intervals (ISIs) between bursts) exceed the intra-burst periods (the sum of ISIs within a burst). To improve the burst-detection self-adaptive algorithm, the maximum interspike interval parameter during the bursting is considered as a constraint condition to identify burst. The results indicated that the improved algorithm efficiently avoided pretermissions and mistakes for burst-detection. It can exactly detect the burst activities of neuron and identify the parameters such as number of bursts, the burst duration. About 93.8% of the average validity for the burst detection was achieved by using the improved method. Comparing with the self-adaptive algorithm, the average validity increased 35.3%.