Thakur Pramodsingh H, Lu Hanzhang, Hsiao Steven S, Johnson Kenneth O
Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, 3400 N Charles Street, Baltimore, MD 21218, USA.
J Neurosci Methods. 2007 May 15;162(1-2):364-76. doi: 10.1016/j.jneumeth.2007.01.023. Epub 2007 Feb 4.
Determination of single unit spikes from multiunit spike trains plays a critical role in neurophysiological coding studies which require information about the precise timing of events underlying the neural codes that are the basis of behavior. Searching for optimal spike detection strategies has therefore been the focus of many studies over the past two decades. In this study we describe and implement an algorithm for the optimal real time detection and classification of neural spikes. The algorithm consists of three steps: noise analysis, template generation and real time detection and classification. The first step involves estimating the background noise statistics. In this step, a "cap-fitting" algorithm is used to automatically detect a spike free segment and then the mean, standard deviation and autocorrelation function of the noise are computed. The second step involves generating optimal templates of the spikes from a segment containing both noise and multiunit activity. In this step, a generalized matched filter is used to isolate a set of preliminary spikes from the noise. The first principal component of previously recorded templates is used as the deterministic signal. The preliminary spikes are then clustered in a sub-space spanned by the first three principal components to form new templates. The third step uses these templates for the real time spike detection and classification. In this step the incoming data are projected into a lower dimensional space that is designed to maximally separate the signal from the noise energy. This algorithm provides an accurate estimate of the signal to noise ratio and provides an accurate estimate of spike times and spike shapes.
从多单元脉冲序列中确定单个单元脉冲在神经生理学编码研究中起着关键作用，这类研究需要了解构成行为基础的神经编码背后事件的精确时间信息。因此，在过去二十年中，寻找最优的脉冲检测策略一直是许多研究的重点。在本研究中，我们描述并实现了一种用于神经脉冲最优实时检测和分类的算法。该算法由三个步骤组成：噪声分析、模板生成以及实时检测和分类。第一步涉及估计背景噪声统计量。在这一步中，使用“帽拟合”算法自动检测无脉冲段，然后计算噪声的均值、标准差和自相关函数。第二步涉及从包含噪声和多单元活动的段中生成脉冲的最优模板。在这一步中，使用广义匹配滤波器从噪声中分离出一组初步脉冲。将先前记录模板的第一主成分用作确定性信号。然后将初步脉冲在前三个主成分所跨越的子空间中聚类以形成新模板。第三步使用这些模板进行实时脉冲检测和分类。在这一步中，将输入数据投影到一个低维空间，该空间旨在最大程度地将信号与噪声能量分离。该算法提供了信噪比的准确估计，并提供了脉冲时间和脉冲形状的准确估计。
