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首页> 外文期刊>Journal of Neuroscience Methods >Spectral representation-analyzing single-unit activity in extracellularly recorded neuronal data without spike sorting.
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Spectral representation-analyzing single-unit activity in extracellularly recorded neuronal data without spike sorting.

机译：频谱表示法分析了细胞外记录的神经元数据中的单个单元活动，而没有峰排序。

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One step in the conventional analysis of extracellularly recorded neuronal data is spike sorting, which separates electrical signal into action potentials from different neurons. Because spike sorting involves human judgment, it can be subjective and time intensive, particularly for large sets of neurons. Here we propose a simple, automated way to construct alternative representations of neuronal activity, called spectral representation (SR). In this approach, neuronal spikes are mapped to a discrete space of spike waveform features and time. Spectral representation enables us to find single-unit stimulus-related changes in neuronal activity without spike sorting. We tested the ability of this method to predict stimuli using both simulated data and experimental data from an auditory mapping study in anesthetized marmoset monkeys. We find that our approach produces more accurate classification of stimuli than spike-sorted data for both simulated and experimental conditions. Furthermore, this method lends itself to automated analysis of extracellularly recorded neuronal ensembles. Additionally, we suggest ways in which these representations can be readily extended to assist in spike sorting and the evaluation of single-neuron peri-stimulus time histograms.
机译：对细胞外记录的神经元数据进行常规分析的一个步骤是尖峰排序，该排序将电信号分离为来自不同神经元的动作电位。由于尖峰排序涉及人类的判断，因此它可能是主观且耗时的，尤其是对于大量神经元而言。在这里，我们提出了一种简单的自动化方法来构造神经活动的替代表示，称为频谱表示（SR）。在这种方法中，神经元尖峰被映射到尖峰波形特征和时间的离散空间。频谱表示使我们能够找到神经元活动中与单单位刺激有关的变化，而无需进行尖峰排序。我们使用模拟数据和来自麻醉using猴的听觉测绘研究的实验数据测试了该方法预测刺激的能力。我们发现，对于模拟和实验条件，我们的方法都能比峰值排序数据产生更准确的刺激分类。此外，该方法有助于对细胞外记录的神经元集合进行自动分析。此外，我们建议可以方便地扩展这些表示形式的方式，以帮助进行尖峰排序和单神经元周围刺激时间直方图的评估。

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