Ohberg F, Johansson H, Bergenheim M, Pedersen J, Djupsjöbacka M
Division of Work Physiology, National Institute of Occupational Health, Umeå, Sweden.
J Neurosci Methods. 1996 Feb;64(2):181-7. doi: 10.1016/0165-0270(95)00132-8.
A multi-channel, real-time, unsupervised spike discriminator was developed in order to reconstruct single spike trains from several simultaneously recorded multi-unit nerve filaments. The program uses a Self Organising Map (SOM) algorithm for the classification of the spikes. In contrast to previous similar techniques, the described method is made for use on a PC, and the method may thus be implemented at relatively low cost. In order to test the accuracy of the program, a robustness test was performed, where noise with different RMS levels was superimposed on the spikes. Furthermore, the maximal classification rate was determined. The program is easy to use, since the only manual inputs needed are the voltage threshold for spike detection, and the number of units present in each recorded nerve filament.
为了从多个同时记录的多单元神经丝中重建单峰序列，开发了一种多通道、实时、无监督的峰值鉴别器。该程序使用自组织映射（SOM）算法对峰值进行分类。与以前的类似技术相比，所描述的方法适用于个人计算机，因此可以以相对较低的成本实现。为了测试该程序的准确性，进行了一项稳健性测试，将具有不同均方根（RMS）水平的噪声叠加在峰值上。此外，还确定了最大分类率。该程序易于使用，因为唯一需要的手动输入是峰值检测的电压阈值以及每个记录的神经丝中存在的单元数量。
