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    Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

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    Author
    Fellous, Jean-Marc cc
    Sapiro, Guillermo
    Rossi, Andrew
    Mayberg, Helen
    Ferrante, Michele
    Affiliation
    Univ Arizona, Dept Psychol & Biomed Engn
    Issue Date
    2019-12-13
    Keywords
    behavioral paradigms
    closed-loop neurostimulation
    computational psychiatry
    data-driven discoveries of brain circuit theories
    explain AI
    machine learning
    neuro-behavioral decisions systems
    
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    Publisher
    FRONTIERS MEDIA SA
    Citation
    Fellous J-M, Sapiro G, Rossi A, Mayberg H and Ferrante M (2019) Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation. Front. Neurosci. 13:1346. doi: 10.3389/fnins.2019.01346
    Journal
    FRONTIERS IN NEUROSCIENCE
    Rights
    Copyright © 2019 Fellous, Sapiro, Rossi, Mayberg and Ferrante. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI’s ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.
    Note
    Open access journal
    ISSN
    1662-4548
    PubMed ID
    31920509
    DOI
    10.3389/fnins.2019.01346
    Version
    Final published version
    Sponsors
    United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH); Computational Psychiatry Program at NIMH; Theoretical and Computational Neuroscience Program at NIMH; 'Machine Intelligence in Healthcare: Perspectives on Trustworthiness, Explainability, Usability and Transparency' workshop at NIH/NCATS; SUBNETS program at DARPA; GARD programs at DARPA
    ae974a485f413a2113503eed53cd6c53
    10.3389/fnins.2019.01346
    Scopus Count
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    UA Faculty Publications

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