Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation.
behavioral paradigms
closed-loop neurostimulation
computational psychiatry
data-driven discoveries of brain circuit theories
explain AI
machine learning
neuro-behavioral decisions systems
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2019
2019
Historique:
received:
11
08
2019
accepted:
29
11
2019
entrez:
11
1
2020
pubmed:
11
1
2020
medline:
11
1
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31920509
doi: 10.3389/fnins.2019.01346
pmc: PMC6923732
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1346Subventions
Organisme : NICHD NIH HHS
ID : P50 HD093074
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH120093
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH121329
Pays : United States
Informations de copyright
Copyright © 2019 Fellous, Sapiro, Rossi, Mayberg and Ferrante.
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