Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Fellous, J-M; Sapiro, G; Rossi, A; Mayberg, H; Ferrante, M

Published Date

  • January 2019

Published In

Volume / Issue

  • 13 /

Start / End Page

  • 1346 -

PubMed ID

  • 31920509

Pubmed Central ID

  • 31920509

Electronic International Standard Serial Number (EISSN)

  • 1662-453X

International Standard Serial Number (ISSN)

  • 1662-4548

Digital Object Identifier (DOI)

  • 10.3389/fnins.2019.01346

Language

  • eng