title: Towards the automatic classification of neurons.
abstract: 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.
authors.affiliation: Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA.
authors.forename: Rubén
authors.initials: R
authors.lastname: Armañanzas
authors.affiliation: Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA. Electronic address: ascoli@gmu.edu.
authors.forename: Giorgio A
authors.initials: GA
authors.lastname: Ascoli
mesh_terms: D000818:Animals; D019295:Computational Biology; D006801:Humans; D000069550:Machine Learning; D009474:Neurons / Q000145:classification* / Q000502:physiology*
keywords: big data; machine learning; metadata; neural classification; standardization