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交叉间隔脉冲序列分析:脉冲产生与双峰时间整合之间的相关性。

A cross-interval spike train analysis: the correlation between spike generation and temporal integration of doublets.

作者信息

Tam D C

机构信息

Department of Biological Sciences, University of North Texas, Denton 76203-5220, USA.

出版信息

Biol Cybern. 1998 Feb;78(2):95-106. doi: 10.1007/s004220050417.

DOI:10.1007/s004220050417
PMID:9525036
Abstract

A stochastic spike train analysis technique is introduced to reveal the correlation between the firing of the next spike and the temporal integration period of two consecutive spikes (i.e., a doublet). Statistics of spike firing times between neurons are established to obtain the conditional probability of spike firing in relation to the integration period. The existence of a temporal integration period is deduced from the time interval between two consecutive spikes fired in a reference neuron as a precondition to the generation of the next spike in a compared neuron. This analysis can show whether the coupled spike firing in the compared neuron is correlated with the last or the second-to-last spike in the reference neuron. Analysis of simulated and experimentally recorded biological spike trains shows that the effects of excitatory and inhibitory temporal integration are extracted by this method without relying on any subthreshold potential recordings. The analysis also shows that, with temporal integration, a neuron driven by random firing patterns can produce fairly regular firing patterns under appropriate conditions. This regularity in firing can be enhanced by temporal integration of spikes in a chain of polysynaptically connected neurons. The bandpass filtering of spike firings by temporal integration is discussed. The results also reveal that signal transmission delays may be attributed not just to conduction and synaptic delays, but also to the delay time needed for temporal integration.

摘要

引入了一种随机脉冲序列分析技术,以揭示下一个脉冲的发放与两个连续脉冲(即一个脉冲对)的时间积分周期之间的相关性。建立神经元之间脉冲发放时间的统计数据,以获得与积分周期相关的脉冲发放条件概率。以参考神经元中连续两个脉冲之间的时间间隔为条件,推导出时间积分周期的存在,以此作为比较神经元中产生下一个脉冲的前提。该分析可以表明比较神经元中的耦合脉冲发放是否与参考神经元中的最后一个或倒数第二个脉冲相关。对模拟和实验记录的生物脉冲序列的分析表明,通过这种方法可以提取兴奋性和抑制性时间积分的效应,而无需依赖任何阈下电位记录。分析还表明,通过时间积分,由随机发放模式驱动的神经元在适当条件下可以产生相当规则的发放模式。在多突触连接的神经元链中,通过脉冲的时间积分可以增强发放的这种规律性。讨论了通过时间积分对脉冲发放进行带通滤波的问题。结果还表明,信号传输延迟可能不仅归因于传导和突触延迟,还归因于时间积分所需的延迟时间。

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