Automated Cell-Type Classification and Death-Detection of Spinal Motoneurons

Abstract

Spinal motoneurons (MNs) play a crucial role in movement control. Decoding the firing activity of spinal MNs could help in real-life challenges, such as enhancing the control of myoelectric prostheses and diagnosing neurodegenerative diseases. In this paper, we propose a machine learning approach to automatically classify MNs based on their firing activity. Applying the proposed approach to data from a MN computational model, the classification accuracy of all examined datasets exceeded 95%. We extended the approach to detecting the death of a given MN type using clustering validity index. Results indicated that 86% of the examined death-detection cases were detected accurately. These results demonstrate that the proposed approach is a successful step in automating neuronal cell-type classification.