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Multi-neuron Action Potentials Recorded with Tetrode are not Instantaneous Mixtures of Single Neuronal Action Potentials

机译:用四极管记录的多神经元动作电位不是单个神经元动作电位的瞬时混合物

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摘要

Multiunit recording with multi-site electrodes in the brain has been widely used in neuroscience studies. After the data recording, neuronal spikes should be sorted according to the pattern of spike waveforms. For the spike sorting, independent component analysis (ICA) has recently been used because ICA has potential for resolving the problem to separate the overlapped multiple neuronal spikes. However the performance of spike sorting by using ICA has not been examined in detail. In this study, we quantitatively evaluate the performance of ICA-based spike sorting method by using simulated multiunit signals. The simulated multiunit signal is constructed by compositing real extracellular action potentials recorded from guinea-pig brain. It is found that the spike sorting by using ICA hardly avoids significant false positive and negative errors due to the cross-talk noise contamination on the separated signals. The cross-talk occurs when the multiunit signal of each recording channel have significant time difference; this situation does not satisfy the assumption of instantaneous source mixture for the major ICA algorithms. Since the channel delay problem is hardly resolved, an ICA algorithm which does not require the instantaneous source mixing assumption would be appropriate for use of spike sorting.
机译:Multiunit录制大脑中的多点电极已广泛用于神经科学研究。在数据记录之后,应根据尖峰波形的图案对神经元尖峰进行分类。对于尖峰分类,最近使用了独立的分量分析(ICA),因为ICA有可能解决问题以分离重叠的多个神经元尖峰。然而,通过使用ICA的尖峰分类的性能尚未详细检查。在本研究中,我们通过使用模拟的多单信号来定量评估基于ICA的峰值分选方法的性能。通过复合从豚鼠脑记录的真正的细胞外动作电位来构建模拟的多单线信号。发现使用ICA的尖峰分类几乎不会避免由于分离信号上的串扰噪声污染而导致的显着误报和负误差。当每个记录通道的多单信号具有显着的时间差时发生串扰;这种情况不满足主要ICA算法的瞬时源混合物的假设。由于信道延迟问题几乎不解决,因此不需要瞬时源混合假设的ICA算法适合于使用尖峰分类。

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