# Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings  
[PERSON] [PERSON]  
1 School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; [EMAIL_ADDRESS] (I.Y.P. ); [EMAIL_ADDRESS] (J.E. ); [EMAIL_ADDRESS] (H.J. ); [EMAIL_ADDRESS] (S.K.) [PERSON]  
1 School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; [EMAIL_ADDRESS] (I.Y.P. ); [EMAIL_ADDRESS] (J.E. ); [EMAIL_ADDRESS] (H.J. ); [EMAIL_ADDRESS] (S.K.) [PERSON]  
1 School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; [EMAIL_ADDRESS] (I.Y.P. ); [EMAIL_ADDRESS] (J.E. ); [EMAIL_ADDRESS] (H.J. ); [EMAIL_ADDRESS] (S.K.) [PERSON]  
1 School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; [EMAIL_ADDRESS] (I.Y.P. ); [EMAIL_ADDRESS] (J.E. ); [EMAIL_ADDRESS] (H.J. ); [EMAIL_ADDRESS] (S.K.) Sanggeon Park  
2 Department of Medical Science, College of Medicine, Catholic Kwandong University, Gangneung 25601, Korea; [EMAIL_ADDRESS] (S.P. ); [EMAIL_ADDRESS] (Y.H.) [PERSON]  
3 Translational Brain Research Center, Catholic Kwandong University, International St. Mary's Hospital, Incheon 22711, Korea 33 Department of Neuroscience, University of Science & Technology, Daejeon 34113, Korea 33 Center for Neuroscience, Korea Institute of Science and Technology, Seoul 02  
The spike sorting procedure is composed of spike detection which filters out noise from the extracellular recordings and sample spike signals, feature extraction which increases efficacy of classification, and lastly classification of the feature domain data. When processed manually, this procedure is costly, not only requiring professional knowledge but also extensive amounts of time. To reduce such cost, various semi-automatic and fully-automatic spike sorting methods are being developed [5; 6; 7; 8], however the show unsatisfactory efficiency and accuracy [9]. With increasing recording periods, and the number of spikes associated with this, the risk of subjective factors and cost also increases [8; 10; 11]. Moreover, when large numbers of spikes are mapped on the feature domain simultaneously, the boundary between clusters becomes unclear for proper classification, and as a result, much of the signal data are thrown away [12]. A classic spike sorting method derived to resolve the abovementioned problems is template matching where first spike templates that represent each neuron and classify each spike according to the similarity between the spike templates is created [13]. A widely used template-making technique is where aligned spikes are first mapped to the feature domain through feature extraction, such as principal component analysis (PCA) [14], and clustered center spikes obtained from clustering techniques, such as k-means clustering [15], are extracted as templates [16; 17]. The similarities between each spike and the template spikes are typically measured by the Euclidean distance [18; 19] and the cross-correlation coefficient [20; 21] between them. Although various studies have reported the classification of spikes according to their shapes and amplitudes, there is as of yet no survey that decides which of them is the most optimal technique [12]. Moreover, the performance of template matching depends heavily on the creation of the templates and generally requires manual intervention and adjustment [9]. Recently, deep learning-based algorithms have shown their efficiency in numerous fields and tasks, especially in classification. Starting with the year 2012 award winning AlexNet [22] from ILSVRC's ImageNet, deep learning-based methods have shown high performance in cases of large 1-dimensional and 2-dimensional data analysis and classification [23]. Its application in the field of neural signal analysis can also be seen. Spike-Deeptector uses deep learning to classify spike signals from artifacts [24], and [PERSON] et al.