Zhang Pu-Ming, Wu Jin-Yong, Zhou Yi, Liang Pei-Ji, Yuan Jing-Qi
Department of Automation, Shanghai Jiao Tong University, Shanghai 200030, China.
J Neurosci Methods. 2004 May 30;135(1-2):55-65. doi: 10.1016/j.jneumeth.2003.12.001.
A new method for spike sorting is proposed which partly solves the overlapping problem. Principal component analysis and subtractive clustering techniques are used to estimate the number of neurons contributing to multi-unit recording. Spike templates (i.e. waveforms) are reconstructed according to the clustering results. A template-matching procedure is then performed. Firstly all temporally displaced templates are compared with the spike event to find the best-fitting template that yields the minimum residue variance. If the residue passes the chi(2)-test, the matching procedure stops and the spike event is classified as the best-fitting template. Otherwise the spike event may be an overlapping waveform. The procedure is then repeated with all possible combinations of two templates, three templates, etc. Once one combination is found, which yields the minimum residue variance among the combinations of the same number of component templates and makes the residue pass the chi(2)-test, the matching procedure stops. It is unnecessary to check the remaining combinations of more templates. Consequently, the computational effort is reduced and the over-fitting problem can be partly avoided. A simulated spike train was used to assess the performance of the proposed method, which was also applied to a real recording of chicken retina ganglion cells.
提出了一种新的尖峰分类方法，该方法部分解决了重叠问题。主成分分析和减法聚类技术用于估计对多单元记录有贡献的神经元数量。根据聚类结果重建尖峰模板（即波形）。然后执行模板匹配过程。首先，将所有时间移位的模板与尖峰事件进行比较，以找到产生最小残差方差的最佳拟合模板。如果残差通过卡方检验，则匹配过程停止，尖峰事件被分类为最佳拟合模板。否则，尖峰事件可能是重叠波形。然后对两个模板、三个模板等的所有可能组合重复该过程。一旦找到一种组合，该组合在相同数量的成分模板组合中产生最小残差方差并使残差通过卡方检验，匹配过程就停止。无需检查更多模板的其余组合。因此，减少了计算量，并且可以部分避免过拟合问题。使用模拟尖峰序列来评估所提出方法的性能，该方法也应用于鸡视网膜神经节细胞的真实记录。
