Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation.
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.
Duke Scholars
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Related Subject Headings
- 5202 Biological psychology
- 3209 Neurosciences
- 1702 Cognitive Sciences
- 1701 Psychology
- 1109 Neurosciences
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Start / End Page
Related Subject Headings
- 5202 Biological psychology
- 3209 Neurosciences
- 1702 Cognitive Sciences
- 1701 Psychology
- 1109 Neurosciences