Takahashi Susumu, Anzai Yuichiro, Sakurai Yoshio
Department of Computer Science, Graduate School of Science and Technology, Keio University, 223-8522, Yokohama, Japan.
Neurosci Res. 2003 Jul;46(3):265-72. doi: 10.1016/s0168-0102(03)00103-2.
Multi-neuronal recording with a tetrode is a powerful technique to reveal neuronal interactions in local circuits. However, it is difficult to detect precise spike timings among closely neighboring neurons because the spike waveforms of individual neurons overlap on the electrode when more than two neurons fire simultaneously. In addition, the spike waveforms of single neurons, especially in the presence of complex spikes, are often non-stationary. These problems limit the ability of ordinary spike sorting to sort multi-neuronal activities recorded using tetrodes into their single-neuron components. Though sorting with independent component analysis (ICA) can solve these problems, it has one serious limitation that the number of separated neurons must be less than the number of electrodes. Using a combination of ICA and the efficiency of ordinary spike sorting technique (k-means clustering), we developed an automatic procedure to solve the spike-overlapping and the non-stationarity problems with no limitation on the number of separated neurons. The results for the procedure applied to real multi-neuronal data demonstrated that some outliers which may be assigned to distinct clusters if ordinary spike-sorting methods were used can be identified as overlapping spikes, and that there are functional connections between a putative pyramidal neuron and its putative dendrite. These findings suggest that the combination of ICA and k-means clustering can provide insights into the precise nature of functional circuits among neurons, i.e. cell assemblies.
使用四极管进行多神经元记录是揭示局部回路中神经元相互作用的一种强大技术。然而，当两个以上神经元同时放电时，由于单个神经元的尖峰波形在电极上重叠，很难检测相邻神经元之间精确的尖峰时间。此外，单个神经元的尖峰波形，尤其是在存在复杂尖峰的情况下，往往是不稳定的。这些问题限制了普通尖峰分类将使用四极管记录的多神经元活动分类为单个神经元成分的能力。虽然使用独立成分分析（ICA）进行分类可以解决这些问题，但它有一个严重的局限性，即分离出的神经元数量必须少于电极数量。通过结合ICA和普通尖峰分类技术（k均值聚类）的效率，我们开发了一种自动程序，以解决尖峰重叠和非平稳性问题，且对分离出的神经元数量没有限制。将该程序应用于实际多神经元数据的结果表明，如果使用普通尖峰分类方法，一些可能被分配到不同簇的异常值可以被识别为重叠尖峰，并且在一个假定的锥体神经元与其假定的树突之间存在功能连接。这些发现表明，ICA和k均值聚类的结合可以深入了解神经元之间功能回路的精确性质，即细胞集合。
