Laboratory of Animal Physiology, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Comput Methods Programs Biomed. 2012 Jul;107(1):28-35. doi: 10.1016/j.cmpb.2011.10.015. Epub 2011 Dec 2.
Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrode's vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. We introduce a graph-theoretical algorithmic procedure that successfully resolves this issue. Dimensionality reduction coupled with a modern, efficient and progressively executable clustering routine proved to achieve higher performance standards than popular spike sorting methods. Our method is validated extensively using simulated data for different levels of SNR.
尖峰排序算法旨在将来自电极附近单个神经元的复杂细胞外信号分解为独立事件。关于实际活跃神经元数量的决策仍然是一个悬而未决的问题，稀疏发射神经元和背景活动是最具影响力的因素。我们引入了一种图论算法程序，成功地解决了这个问题。降维与现代、高效和逐步执行的聚类例程相结合，被证明比流行的尖峰排序方法具有更高的性能标准。我们的方法使用不同 SNR 水平的模拟数据进行了广泛验证。
