摘要
神经元放电脉冲序列的研究可能与神经中枢信息处理和传播机制的研究有关。在神经元放电脉冲序列间隔中存在着一些重复出现的脉冲片段,称为优势模式。对优势模式的识别、检测是长期以来电生理学的一个重要研究领域。用量化蒙特卡罗法和模板匹配法能成功地从原始数据中识别出优势模式,并统计出这些优势模式在神经元放电脉冲序列中出现的次数。本文给出了算法、程序的流程和结构,并给出了蓝斑神经元优势模式的检测结果。
The modern neuroclectrical physiology indicates that there are certain patterns in neural spike train, which appear more frequently than others. There fa-vored patterns may related to the mechanism of neural information processing in central nervous system. In this paper, the quantized Monte Carlo method and temp-late method which are used in favored pattern recognition are described, The Monte Carlo method determines favored patterns or the possible favored patterns- The te-mplate method is used for choosing the favored patterns from the candidates and counting the number of the patterns in the spik train. This work will be pursued for extracting the patterns in some complicated spik train with very different discharge cycles.
关键词
神经元
放电
脉冲序列
模式识别
Neural spike train
Favored patterns
Monte Carlo method
