CAJ | 학술논문

　　神经元集群编码和 spike train 分析是神经信息处理的关键问题。该文介绍了一种利用高阶多维泊松模型对spike train进行分类预测的方法，并从spike的强度分布、匹配准确性和集成策略上进行了数学论证。最后利用该方法在大鼠U迷宫实验中选取20组作为训练集进行分类测试，实验结果表明，利用该方法得到的分类准确率在97%左右。
　　신경원집군편마화 spike train 분석시신경신식처리적관건문제。해문개소료일충이용고계다유박송모형대spike train진행분류예측적방법，병종spike적강도분포、필배준학성화집성책략상진행료수학론증。최후이용해방법재대서U미궁실험중선취20조작위훈련집진행분류측시，실험결과표명，이용해방법득도적분류준학솔재97%좌우。
Neural population encoding and analysis of spike train play an important role in the field of neural inforamtion processing. In this study, a classification method of spike train is proposed based on high-order multiple Possion model, and a mathematic deduction is made in the spike instensity distribution, accuracy of matching and integration strategy, respectively. Finally, 20 trails, as a traing set, are applied to experiment of U maze of mouse. The result demonstrates that the accuracy rate of the classification method is about 97%.