Spike sorting refers to the technique of detecting signals generated by single neurons from multi-neuron recordings and is a valuable tool for analyzing the relationships between individual neuronal activity patterns and specific behaviors. Since the precision of spike sorting affects all subsequent analyses, sorting accuracy is critical. Many semi-automatic to fully-automatic spike sorting algorithms have been developed. However, due to unsatisfactory classification accuracy, manual sorting is preferred by investigators despite the intensive time and labor costs. Thus, there still is a strong need for fully automatic spike sorting methods with high accuracy. Various machine learning algorithms have been developed for feature extraction but have yet to show sufficient accuracy for spike sorting. Here we describe a deep learning-based method for extracting features from spike signals using an ensemble of auto-encoders, each with a distinct architecture for distinguishing signals at different levels of resolution. By utilizing ensemble of auto-encoder ensemble, where shallow networks better represent overall signal structure and deep networks better represent signal details, extraction of high-dimensional representative features for improved spike sorting performance is achieved. The model was evaluated on publicly available simulated datasets and single-channel and 4-channel tetrode in vivo datasets. Our model not only classified single-channel spikes with varying degrees of feature similarities and signal to noise levels with higher accuracy, but also more precisely determined the number of source neurons compared to other machine learning methods. The model also demonstrated greater overall accuracy for spike sorting 4-channel tetrode recordings compared to single-channel recordings.

尖峰排序是指从多神经元记录中检测单个神经元生成的信号的技术，是分析单个神经元活动模式与特定行为之间关系的有价值的工具。由于加标排序的精度会影响所有后续分析，因此分类精度至关重要。已经开发了许多半自动到全自动尖峰分选算法。但是，由于分类精度不理想，尽管需要大量的时间和人工，但调查人员还是首选手动分类。因此，仍然强烈需要高精度的全自动尖峰分选方法。已经开发了用于特征提取的各种机器学习算法，但尚未显示出足够的精度来进行尖峰排序。在这里，我们描述了一种基于深度学习的方法，该方法使用一组自动编码器从峰值信号中提取特征，每个编码器均具有用于区分不同分辨率级别信号的独特体系结构。通过利用自动编码器集成体，其中浅层网络更好地表示整体信号结构，而深层网络更好地表示信号细节，可以实现高维代表性特征的提取，从而提高了尖峰分类性能。在公开可用的模拟数据集以及单通道和四通道四极杆上评估了该模型 在浅层网络更好地表示整体信号结构的地方，而深层网络则更好地表示信号的细节，实现了高维代表性特征的提取，从而提高了尖峰分类性能。在公开可用的模拟数据集以及单通道和四通道四极杆上评估了该模型 在浅层网络更好地表示整体信号结构的地方，而深层网络则更好地表示信号的细节，实现了高维代表性特征的提取，从而提高了尖峰分类性能。在公开可用的模拟数据集以及单通道和四通道四极杆上评估了该模型体内数据集。与其他机器学习方法相比，我们的模型不仅对具有相似程度的特征相似度和信噪比的单通道尖峰进行了分类，而且还更精确地确定了源神经元的数量。与单通道记录相比，该模型还显示了4通道四极体尖峰分选记录的更高总体精度。