Burst and oscillation as disparate neuronal properties
Burst and oscillation as disparate neuronal properties
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DOI：
10.1016/0165-0270(96)00081-7
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发表时间：
1996-10-01
影响因子：
3
通讯作者：
Vitek, JL
中科院分区：
文献类型：
--
作者：
Kaneoke, Y;Vitek, JL
我们已经发展出检测和辨别神经元活动的爆发和振荡模式的方法。在它们中，突发周期被定义为一个间隔，在这个间隔中，峰值的数量明显高于峰值序列中的其他间隔。振荡被定义为在其自相关图中检测到显著周期性的尖峰序列。我们的突发检测方法的主要特点是使用放电密度（即短间隔内的尖峰数量）而不是尖峰间隔。这使人们能够评估在尖峰序列中出现爆发期的可能性。我们使用朗姆周期图来检测自相关图中的周期性。这种方法给出了周期性检测的一个意义，并且能够在自相关图中检测多个频率。讨论了这些方法的优点，并与其他用于检测爆破和振荡活动的方法进行了比较。
We have developed methods to detect and discern burst and oscillatory patterns of neuronal activity. Tn them, a burst period is defined as an interval in which there are a significantly higher number of spikes as compared to other intervals in the spike train. Oscillation is defined as a spike train in which significant periodicity is detected in its autocorrelogram. The main feature of our burst detection method is that discharge density (i.e., the number of spikes in a short interval) is used instead of the interspike interval. This enables one to assess the likelihood of having burst periods in a spike train. We use the Lomb periodogram to detect periodicity in an autocorrelogram. This method gives one significance of periodicity detected and enables the detection of multiple frequencies in an autocorrelogram. The advantage of these methods is discussed in comparison with the other methods used to detect bursting and oscillatory activity.