Improving the Accuracy of Neurons Spike Sorting by Using Supervised Machine Learning

Abstract

The brain is important for both the functioning and reasoning ability of the body. It plays a fundamental role in the coordination of body functioning as well as reasoning and thinking. To understand how the brain is working, we need to know how neurons communicate with each other by firing (Action potential) which is known as spike. To record these activities neurologists used the multi-electrode which record thousands of spikes at the same time. Therefore, neurologists used the Spike Sorting Algorithm (SSA) to know which spike belongs to which neuron. The accuracy of the spike sorting is the most important point. Accordingly, machine learning is used to improve the accuracy of the spike sorting. In this paper, the Principal Component Analysis (PCA) is implemented to extract features and for clustering step, the Supervised Machine Learning is applied by using the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to compare the accuracy of the supervised clustering with the Template Matching method. A comparison between the results of Applied Machine Learning is achieved at different levels of noise to check the accuracy of each algorithm. The results showed that when the noise level was low, KNN accuracy reached 100% while SVM reached 95% and template 100 %. However, when the noise level increased to 0.5, the accuracy of KNN became 94 % and template 85.6 % and SVM 90 %.