Roberts W M
Biol Cybern. 1979 Nov 2;35(2):73-80. doi: 10.1007/BF00337433.
Statistically optimal methods for identifying single unit activity in multiple unit recordings are discussed. These methods take into account both the nerve impulse waveforms and the firing patterns of the units. A generalized least-squares fit procedure is shown to be the optimal recognition scheme under some reasonable statistical assumptions, but the amount of computation becomes prohibitively large when the method is applied to the problem of sorting superimposed waveforms. A linear filter technique which relies on simultaneous recording from several electrodes is shown to give good separation of superimposed waveforms. An iterative recognition procedure can be applied to improve the results and reduce the number of recording electrodes required.
讨论了用于识别多单元记录中单个单元活动的统计最优方法。这些方法同时考虑了神经冲动波形和单元的放电模式。在一些合理的统计假设下，广义最小二乘拟合过程被证明是最优识别方案，但当该方法应用于叠加波形的分类问题时，计算量会变得大到令人望而却步。一种依赖于从多个电极同时记录的线性滤波技术被证明能很好地分离叠加波形。可以应用迭代识别过程来改善结果并减少所需的记录电极数量。
