Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

• Published in: Frontiers in neuroscience Vol. 13; p. 1346
• Main Authors: Fellous, Jean-Marc, Sapiro, Guillermo, Rossi, Andrew, Mayberg, Helen, Ferrante, Michele
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 13.12.2019; Frontiers Media S.A
• Subjects: Algorithms; Artificial intelligence; behavioral paradigms; Brain research; closed-loop neurostimulation; computational psychiatry; Computer engineering; Decision making; explain AI; Learning algorithms; Machine learning; Mental disorders; Mental health; Nervous system; Neural networks; neuro-behavioral decisions systems; Neuroscience; Neurosciences; Psychiatry; Therapeutic applications; United States > US; computational psychiatry; behavioral paradigms; closed-loop neurostimulation; explain AI; neuro-behavioral decisions systems; machine learning; data-driven discoveries of brain circuit theories
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2019.01346

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.