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J Neurosci. 2018 Feb 14;38(7):1601-1607. doi: 10.1523/JNEUROSCI.0508-17.2018. Epub 2018 Jan 26.

A Shared Vision for Machine Learning in Neuroscience.

Author information

1
Department of Neurobiology, kafui.dzirasa@duke.edu mai.anh.vu@duke.edu.
2
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland 21250.
3
School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138.
4
Department of Neuroscience, New York University School of Medicine, New York, New York 10016.
5
Department of Civil and Environmental Engineering.
6
Department of Biostatistics and Bioinformatics.
7
Department of Statistical Sciences.
8
Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, New York, 10065.
9
Department of Electrical and Computer Engineering.
10
Department of Computer Science, Duke University, Durham, North Carolina 27710.
11
Department of Psychiatry and Weill Institute for Neuroscience, University of California-San Francisco, San Francisco, California 94158.
12
Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts 02129, and.
13
Department of Psychiatry, Neurology, and Radiology, Emory University, Atlanta, Georgia 30322.
14
Department of Psychiatry and Behavioral Sciences.

Abstract

With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.

KEYWORDS:

explainable artificial intelligence; machine learning; reinforcement learning

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