Gozani S N, Miller J P
Department of Molecular and Cell Biology, University of California, Berkeley 94720.
IEEE Trans Biomed Eng. 1994 Apr;41(4):358-72. doi: 10.1109/10.284964.
We describe advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. The programs are capable of detecting and classifying the spikes from multiple, simultaneously active neurons, even in situations where there is a high degree of spike waveform superposition on the recording channels. The protocols are based on the derivation of an optimal linear filter for each individual neuron. Each filter is tuned to selectively respond to the spike waveform generated by the corresponding neuron, and to attenuate noise and the spike waveforms from all other neurons. The protocol is essentially an extension of earlier work [1], [13], [18]. However, the protocols extend the power and utility of the original implementations in two significant respects. First, a general single-pass automatic template estimation algorithm was derived and implemented. Second, the filters were implemented within a software environment providing a greatly enhanced functional organization and user interface. The utility of the analysis approach was demonstrated on samples of multiunit electrophysiological recordings from the cricket abdominal nerve cord.
我们描述了用于多通道电生理记录中神经元尖峰波形辨别与分类的先进协议。这些程序能够检测并分类来自多个同时活跃神经元的尖峰，即使在记录通道上存在高度的尖峰波形叠加的情况下也是如此。该协议基于为每个单独神经元推导的最优线性滤波器。每个滤波器经过调整，以选择性地响应相应神经元产生的尖峰波形，并衰减来自所有其他神经元的噪声和尖峰波形。该协议本质上是早期工作[1]、[13]、[18]的扩展。然而，该协议在两个重要方面扩展了原始实现的能力和实用性。第一，推导并实现了一种通用的单通道自动模板估计算法。第二，滤波器在一个软件环境中实现，该环境提供了大大增强的功能组织和用户界面。通过蟋蟀腹神经索的多单元电生理记录样本证明了该分析方法的实用性。
