A shared vision for machine learning in neuroscience

Mai Anh T. Vu, Tülay Adalı, Demba Ba, György Buzsáki, David Carlson, Katherine Heller, Conor Liston, Cynthia Rudin, Vikaas S. Sohal, Alik S. Widge, Helen S. Mayberg, Guillermo Sapiro, Kafui Dzirasa

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

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 eperimental 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 eists 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.

Original languageEnglish (US)
Pages (from-to)1601-1607
Number of pages7
JournalJournal of Neuroscience
Volume38
Issue number7
DOIs
StatePublished - Feb 14 2018

Bibliographical note

Funding Information:
This work was supported by National Institute of Mental Health Grant R01MH099192-05S2 to K.D., National Science Foundation GRFP to M.-A.T.V., and Duke Katherine Goodman Stern Fellowship to M.-A.T.V. Many of the themespresentedinthismanuscriptreflectareasofconsensusthatemergedfromtheNationalInstitutesofMental Health-sponsored Explainable Artificial Intelligence Meeting on November 10, 2017 in Washington, DC (co-organized by Michele Ferrante, Guillermo Sapiro, and Helen S. Mayberg). We thank Shabnam Hakimi, Stephen D. Mague,R.AlisonAdcock,NeilM.Gallagher,AustinTalbot,SashaBurwell,andRainboHultmanforcommentsonthis manuscript. The authors declare no competing financial interests.

Keywords

  • Eplainable artificial intelligence
  • Machine learning
  • Reinforcement learning

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