Brain-like functional specialization emerges spontaneously in deep neural networks
Name
sciadv.abl8913.pdf
Description
Published version
Size
787.25 KB
Format
Adobe PDF
Checksum (MD5)
1c2a8d9495af5d91084a19d9537b88c8
Author(s)
Dobs, Katharina
Martinez, Julio
Kell, Alexander JE
Kanwisher, Nancy
Date Issued
2022
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Dobs, Katharina, Martinez, Julio, Kell, Alexander JE and Kanwisher, Nancy. 2022. "Brain-like functional specialization emerges spontaneously in deep neural networks." Science Advances, 8 (11).
Version
Final published version
Abstract
The human brain contains multiple regions with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what others are thinking. However, it remains unclear why the cortex exhibits this high degree of functional specialization in the first place. Here, we consider the case of face perception using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects a computational optimization for the broader problem of visual recognition of faces and other visual categories. We find that networks trained on object recognition perform poorly on face recognition and vice versa and that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
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DOI of Published Version
https://doi.org/10.1126/SCIADV.ABL8913
