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通过间隔预测和形态学识别多纤维神经尖峰序列。

Identification of multifiber neural spike trains by interval prediction and morphology.

作者信息

Lu J P, Myers G A

出版信息

Biomed Sci Instrum. 1989;25:23-6.

PMID:2545291
Abstract

Interpretation of neurograms from multifiber preparations is usually limited to forming a grand running average. In so doing, individual nerve activity is lost. This information is important in studying nervous system activity. We have developed a method to find the rates of constituent nerve fibers using linear one-step prediction and cross correlation between the spike template and instantaneous spikes. Some size discrimination is employed, as is a memory to mark spikes which are "accounted for." Thus we are able to parse out the several rates which make up an aggregate spike train. Our results show that some afferent baroreceptor fibers may increase rate, while others may decrease rate, in response to holding carotid sinus pressure at a high level for 10 minutes.

摘要

对多纤维制剂的神经图的解读通常仅限于形成一个总体移动平均值。这样做会丢失个体神经活动。这些信息在研究神经系统活动中很重要。我们已经开发出一种方法,利用线性单步预测以及峰值模板与瞬时峰值之间的互相关来找出组成神经纤维的发放率。采用了一些大小辨别方法,还有一个用于标记已“处理”峰值的存储器。因此,我们能够解析出构成复合峰值序列的几种发放率。我们的结果表明,在将颈动脉窦压力维持在高水平10分钟的情况下,一些传入压力感受器纤维的发放率可能会增加,而其他纤维的发放率可能会降低。

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Biomed Sci Instrum. 1989;25:23-6.
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