Before revision:  These methods, however, demand a lot of human intervention and observation, they are time-consuming and regarding the feature extraction algorithms it is not clear or obvious what are the best featuresthat define a neuronal cell class. In this work we examine three different deep learning models aiming at an automated neuronal cell-type classification and compare their performance.
After revision:  These methods, however, demand a lot of human intervention . In this work we establish the first, automated cell-type classification method that relies on neuronal activity rather than molecular or cellular features. We test our method on a real-world dataset comprising of raw calcium activity signals for four neuronal types. We compare the performance of three different deep learning models aiming at an automated neuronal cell-type classification and compare their performance.