Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence
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1-s2.0-S089662732030605X-main.pdf
Description
Published version
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
5df96df0c0e5f0d789cb9c9246443027
Author(s)
Schrimpf, Martin
Kubilius, Jonas
Lee, Michael J
Ratan Murty, N Apurva
Ajemian, Robert J.
DiCarlo, James J
Date Issued
2020
Journal
Neuron
Publisher
Elsevier BV
Version
Final published version
Abstract
© 2020 The Author(s) 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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Center for Brains, Minds, and Machines
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
10.1016/J.NEURON.2020.07.040
https://doi.org/10.1016/J.NEURON.2020.07.040
