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

Towards the automatic classification of neurons.

Author information

1
Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA.
2
Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA. Electronic address: ascoli@gmu.edu.

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.

KEYWORDS:

big data; machine learning; metadata; neural classification; standardization

PMID:
25765323
PMCID:
PMC4417416
DOI:
10.1016/j.tins.2015.02.004
[Indexed for MEDLINE]
Free PMC Article

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