Spike detection from noisy neural data in linear-probe recordings
Spike detection from noisy neural data in linear-probe recordings
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DOI：
10.1111/ejn.12614
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发表时间：
2014-06-01
影响因子：
3.4
通讯作者：
Fukai, Tomoki
中科院分区：
文献类型：
--
作者：
Takekawa, Takashi;Ota, Keisuke;Fukai, Tomoki
使用多通道细胞外电极同时记录多个神经元活动被广泛用于研究大脑神经回路的信息处理。在该方法中，包含多个相邻或远处神经元的尖峰事件的记录信号必须被正确地分类为各个神经元的尖峰序列，并且已经提出了多种用于这种尖峰排序的方法。然而，尖峰排序在计算上很困难，因为记录的信号经常受到生物噪声的污染。在这里，我们提出了一种新颖的尖峰检测方法，这是尖峰排序的第一阶段，因此至关重要地决定了整体排序性能。我们的方法利用细胞外记录数据模型，通过检测带通滤波数据的峰值来考虑尖峰波形的变化，例如尖峰的宽度和幅度。我们表明，新方法通过增加干净排序的神经元的数量，显着提高了多通道电极记录的性价比。
Simultaneous recordings of multiple neuron activities with multi-channel extracellular electrodes are widely used for studying information processing by the brain's neural circuits. In this method, the recorded signals containing the spike events of a number of adjacent or distant neurons must be correctly sorted into spike trains of individual neurons, and a variety of methods have been proposed for this spike sorting. However, spike sorting is computationally difficult because the recorded signals are often contaminated by biological noise. Here, we propose a novel method for spike detection, which is the first stage of spike sorting and hence crucially determines overall sorting performance. Our method utilizes a model of extracellular recording data that takes into account variations in spike waveforms, such as the widths and amplitudes of spikes, by detecting the peaks of band-pass-filtered data. We show that the new method significantly improves the cost-performance of multi-channel electrode recordings by increasing the number of cleanly sorted neurons.