Musial P G, Baker S N, Gerstein G L, King E A, Keating J G
Department of Neuroscience, University of Pennsylvania, A306 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104-6085, USA.
J Neurosci Methods. 2002 Mar 30;115(1):29-43. doi: 10.1016/s0165-0270(01)00516-7.
Recordings of spike trains made with microwires or silicon electrodes include more noise from various sources that contaminate the observed spike shapes compared with recordings using sharp microelectrodes. This is a particularly serious problem if spike shape sorting is required to separate the several trains that might be observed on a particular electrode. However, if recordings are made with an array of such electrodes, there are several mathematical methods to improve the effective signal (spikes) to noise ratio, thus considerably reducing inaccuracy in spike detection and shape sorting. We compare the theoretical basis of three such methods and evaluate their performance with simulated and real data.
与使用尖锐微电极的记录相比,用微丝或硅电极进行的尖峰序列记录包含更多来自各种来源的噪声,这些噪声会污染观察到的尖峰形状。如果需要通过尖峰形状分类来分离在特定电极上可能观察到的多个序列,这将是一个特别严重的问题。然而,如果使用这样的电极阵列进行记录,有几种数学方法可以提高有效信号(尖峰)与噪声的比率,从而大大降低尖峰检测和形状分类中的不准确性。我们比较了三种此类方法的理论基础,并使用模拟数据和真实数据评估了它们的性能。