Local shuffling of spike trains boosts the accuracy of spike train spectral analysis
Local shuffling of spike trains boosts the accuracy of spike train spectral analysis
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DOI：
10.1152/jn.00055.2005
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发表时间：
2006-05-01
影响因子：
2.5
通讯作者：
Bar-Gad, I
中科院分区：
文献类型：
--
作者：
Rivlin-Etzion, M;Ritov, Y;Bar-Gad, I
神经元锋电位序列的频谱分析是了解神经元活动特征的重要工具，它可以提供正常和病理性周期振荡现象的见解。然而，不应期在尖峰脉冲串放电率中产生高频调制，因为放电率的任何上升都会导致后续时间仓的下降，从而导致频谱结构的多方面修改。因此，尖峰活动的功率谱（自谱）显示相对于较低频率的高频能量升高。频谱失真在高放电率和长不应期的神经元中更占主导地位，并可能导致对低频振荡的识别减少（例如帕金森病基底神经节和丘脑典型的5至10 Hz爆发振荡）。我们提出了一个补偿过程，使用洗牌的interspike间隔（ISI）的可靠识别的振荡在整个频率范围内。这种补偿通过局部混洗得到进一步改善，局部混洗保留了可能在全局混洗中丢失的放电速率的缓慢变化。神经元对的互谱也同样失真，而不管它们的相关水平如何。因此，识别低频同步振荡，即使是由一个电极记录的两个神经元，改善ISI洗牌。ISI本地洗牌计算的置信限，是基于一阶统计的尖峰列车，从而提供了一个可靠的估计的自动和交叉频谱的尖峰列车，使其成为一个最佳的工具振荡神经元现象的生理研究。
Spectral analysis of neuronal spike trains is an important tool in understanding the characteristics of neuronal activity by providing insights into normal and pathological periodic oscillatory phenomena. However, the refractory period creates high-frequency modulations in spike-train firing rate because any rise in the discharge rate causes a descent in subsequent time bins, leading to multifaceted modifications in the structure of the spectrum. Thus the power spectrum of the spiking activity ( autospectrum) displays elevated energy in high frequencies relative to the lower frequencies. The spectral distortion is more dominant in neurons with high firing rates and long refractory periods and can lead to reduced identification of low-frequency oscillations ( such as the 5- to 10-Hz burst oscillations typical of Parkinsonian basal ganglia and thalamus). We propose a compensation process that uses shuffling of interspike intervals (ISIs) for reliable identification of oscillations in the entire frequency range. This compensation is further improved by local shuffling, which preserves the slow changes in the discharge rate that may be lost in global shuffling. Cross-spectra of pairs of neurons are similarly distorted regardless of their correlation level. Consequently, identification of low-frequency synchronous oscillations, even for two neurons recorded by a single electrode, is improved by ISI shuffling. The ISI local shuffling is computed with confidence limits that are based on the first-order statistics of the spike trains, thus providing a reliable estimation of auto-and cross-spectra of spike trains and making it an optimal tool for physiological studies of oscillatory neuronal phenomena.