Axonal tree morphology and signal propagation dynamics improve neuronal classification
Classification of neurons into specific subtypes is essential for better understanding of brain function and information transmission. Despite continuous progress, there is still no consensus regarding categorizing neuron taxonomy into proper subtypes. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the general axonal projection layout. In this study, we support the use of a morphology-based classification approach, focusing on the axonal tree. We demonstrate that utilizing the geometrical parameters of axonal tree structures significantly improves neuronal classification compared to the dendritic tree classification. Furthermore, we used neuronal activity patterns to classify interneurons into subtypes as well. Simulations of the activity along ramified axonal trees indicate that the axonal branching geometry may yield diverse responses in different subtrees. The classification schemes introduced here can be utilized to robustly classify neuronal subtypes in a functionally relevant manner. Our results open the door for deducing functionality from anatomical data.