基于贝叶斯原理的多维Spike Train分类预测模型

摘要

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%.%　　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。