Variability of extracellular spike waveforms of cortical neurons
Variability of extracellular spike waveforms of cortical neurons
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DOI：
10.1152/jn.1996.76.6.3823
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发表时间：
1996-12-01
影响因子：
2.5
通讯作者：
Kleinfeld, D
中科院分区：
文献类型：
--
作者：
Fee, MS;Mitra, PP;Kleinfeld, D
1.在这里，我们研究的变异性细胞外记录的动作电位。我们的工作的动机，在一定程度上，需要构建有效的算法来分类单单位波形从多单位记录。我们使用微电极对（立体电极）记录从清醒，行为大鼠的初级躯体感觉皮层。我们的数据包括细胞外活动的连续记录和细胞外尖峰的分段记录。谱和主成分技术用于分析平均单单元波形、单单元波形的不同实例之间的可变性以及潜在的背景活动。单个单元波形的不同实例之间的可变性的频谱不是白色的，并且福尔斯在1 kHz以上下降，频率依赖性大致为f（-2）。这个频谱不同于平均尖峰波形的频谱，平均尖峰波形大致随着f（-4）而福尔斯，但是基本上与背景活动的频谱相同。在10 μ m尺度上的变异性的空间相干性也在高频率下福尔斯。单个单元波形的不同实例之间的可变性由相对少量的主成分支配。因此，在尖峰波形的簇中存在大的各向异性。背景噪声不能表示为平稳高斯随机过程。特别是，我们观察到，连续20毫秒的间隔之间的频谱变化显着。此外，背景活动中的总功率表现出比平稳高斯串联过程更大的波动。大约有一半的单单位尖峰波形表现出系统的变化作为一个函数的interspike间隔。虽然这导致波形空间中的非高斯分布，但该分布可以通过尖峰间期的标量函数来建模。我们使用一组44个平均单单位波形来定义尖峰波形之间的差异空间。该特征与背景活动的特征一起用于构建优化单个单元波形之间的差异检测的滤波器。此外，定义了表征可检测性的信息论度量。
1. Here we study the variability in extracellular records of action potentials. Our work is motivated, in part, by the need to construct effective algorithms to classify single-unit waveforms from multiunit recordings.2. We used microwire electrode pairs (stereotrodes) to record from primary somatosensory cortex of awake, behaving rat. Our data consist of continuous records of extracellular activity and segmented records of extracellular spikes. Spectral and principal component techniques are used to analyze mean single-unit waveforms, the variability between different instances of a single-unit waveform, and the underlying background activity.3. The spectrum of the variability between different instances of a single-unit waveforms is not white, and falls off above 1 kHz with a frequency dependence of roughly f(-2). This spectrum is different from that of the mean spike waveforms, which falls off roughly as f(-4), but is essentially identical with the spectrum of background activity. The spatial coherence of the variability on the 10-mu m scale also falls off at high frequencies.4. The variability between different instances of a single-unit waveform is dominated by a relatively small number of principal components. As a consequence, there is a large anisotropy in the cluster of the spike waveforms.5. The background noise cannot be represented as a stationary Gaussian random process. In particular, we observed that the spectrum changes significantly between successive 20-ms intervals. Furthermore, the total power in the background activity exhibits larger fluctuations than is consistent with a stationary Gaussian tandem process.6. Roughly half of the single-unit spike waveforms exhibit systematic changes as a function of the interspike interval. Although this results in a non-Gaussian distribution in the space of waveforms, the distribution can be modeled by a scalar function of the interspike interval.7. We use a set of 44 mean single-unit waveforms to define the space of differences between spike waveforms. This characterization, together with that of the background activity, is used to construct a filter that optimizes the detection of differences between single-unit waveforms. Further, an information theoretic measure is defined that characterizes the detectability.