Towards the automatic classification of neurons

Trends Neurosci. 2015 May;38(5):307-18. doi: 10.1016/j.tins.2015.02.004. Epub 2015 Mar 9.


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.

Keywords: big data; machine learning; metadata; neural classification; standardization.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Animals
  • Computational Biology*
  • Humans
  • Machine Learning*
  • Neurons / classification*
  • Neurons / physiology*