Skip to Main Content
Neuronal morphology, which is closely related to neuronal characteristics and functions, is complex and diversified, and it has gradually attracted more and more neuroscientists to study on it. As the neuronal classification is a basic point in neuronal study but no existing universal methods take neuronal morphology into consideration, this paper dedicates to fill this blank. Firstly, this paper used Principal Component Analysis (PCA) method to select five key features from twenty neuronal morphologic features. With these key features, this paper leveraged hierarchical clustering to cluster sixty neurons randomly selected from the NeuroMorpho.Org website. As a result, these neurons were divided into four categories. Finally, we devised a new naming scheme by the range of key features' values. Experiments indicated that this classification can effectively distinguish neurons by morphology. At last, this paper discussed and analyzed the anomaly that occurs after a large number of classification experiments.