摘要细节小说神经峰值概率统计方法进行自动排序,使用随机点过程模型的神经高峰火车和参数化的动作电位波形。小说可能性模型观察发射倍隐藏神经高峰列车派生的聚合,以及迭代过程为集群数据和发现的可能性最大化的参数。执行和评估方法完全semiartificial标记数据集和部分标记的数据集从大鼠的海马细胞外电动痕迹。(即在条件相对较高的困难。,w我th additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset.