Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence.
computational neuroscience
integrative benchmarking
neurally mechanistic modeling
ventral stream
Journal
Neuron
ISSN: 1097-4199
Titre abrégé: Neuron
Pays: United States
ID NLM: 8809320
Informations de publication
Date de publication:
11 11 2020
11 11 2020
Historique:
received:
01
03
2020
revised:
21
07
2020
accepted:
29
07
2020
pubmed:
13
9
2020
medline:
29
12
2020
entrez:
12
9
2020
Statut:
ppublish
Résumé
A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains of human intelligence as executable, neurally mechanistic models. Years of research have led to models that capture experimental results in individual behavioral tasks and individual brain regions. We here advocate for taking the next step: integrating experimental results from many laboratories into suites of benchmarks that, when considered together, push mechanistic models toward explaining entire domains of intelligence, such as vision, language, and motor control. Given recent successes of neurally mechanistic models and the surging availability of neural, anatomical, and behavioral data, we believe that now is the time to create integrative benchmarking platforms that incentivize ambitious, unified models. This perspective discusses the advantages and the challenges of this approach and proposes specific steps to achieve this goal in the domain of visual intelligence with the case study of an integrative benchmarking platform called Brain-Score.
Identifiants
pubmed: 32918861
pii: S0896-6273(20)30605-X
doi: 10.1016/j.neuron.2020.07.040
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
413-423Informations de copyright
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.