Towards the automatic classification of neurons

• The automation of neuron type classification is advancing ever more rapidly.
• Accelerating data collection makes machine learning necessary for neuronal classification.
• We review analysis approaches, algorithm classes, and available resources.
• Opportunities include software development, data standardization, and integration.

The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and the availability of suitable data and resources, highlighting prominent challenges and opportunities. The effective solution of the neuronal classification problem will require continuous development of computational methods, high-throughput data production, and systematic metadata organization to enable cross-laboratory integration.