Abstract：
Spike sorting plays an essential role to obtain electrophysiological activity of single neuron in the fields of neural signal decoding. With the development of electrode array, large numbers of spikes are recorded simultaneously, which rises the need for accurate automatic and generalization algorithms. Hence, this paper proposes a spike sorting model with convolutional neural network (CNN) and a spike classification model with combination of CNN and Long-Short Term Memory (LSTM). The recall rate of our detector could reach 94.40% in low noise level dataset. Although the recall declined with the increasing noise level, our model still presented higher feasibility and better robustness than other models. In addition, the results of our classification model presented an accuracy of greater than 99% in simulated data and an average accuracy of about 95% in experimental data, suggesting our classifier outperforms the current \"WMsorting\" and other deep learning models. Moreover, the performance of our whole algorithm was evaluated through simulated data and the results shows that the accuracy of spike sorting reached about 97%. It is noteworthy to say that, this proposed algorithm could be used to achieve accurate and robust automated spike detection and spike classification.
摘要：
在神经信号解码领域，尖峰分选对获得单个神经元的电生理活动起着至关重要的作用。随着电极阵列的发展,同时记录大量的尖峰，这增加了对精确的自动和泛化算法的需求。因此，提出了一种基于卷积神经网络(CNN)的尖峰分类模型和基于CNN和长短期记忆(LSTM)相结合的尖峰分类模型。在低噪声水平的数据集中，我们的探测器的召回率可以达到94.40%。尽管召回率随着噪音水平的增加而下降，我们的模型仍然比其他模型具有更高的可行性和更好的鲁棒性。此外,我们的分类模型的结果表明，模拟数据的准确率大于99%，实验数据的平均准确率约为95%，这表明我们的分类器优于当前的“WMsorting”和其他深度学习模型。此外，通过仿真数据评估了整个算法的性能，结果表明，尖峰排序的准确率达到了97%左右。值得注意的是,该算法可用于实现准确、鲁棒的自动尖峰检测和尖峰分类。
