@article{28e69e03374642de89a08a7124408827,
title = "Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks",
abstract = "To guide adaptive behavior and support predictions in real-life contexts, the brain may rely on opaque, over-parameterized models capable of directly fitting to the multidimensional world, while being blind—like evolution—to the underlying rules and causes.",
keywords = "evolution, experimental design, interpolation, learning, neural networks",
author = "Uri Hasson and Nastase, {Samuel A.} and Ariel Goldstein",
note = "Funding Information: We thank Hezy Yeshurun for insightful discussion early on, as well as Galia Avidan, Eran Malach, Bruno Galantucci, Asif A. Ghazanfar, Rita Goldstein, Liat Hasenfratz, Hanna Hillman, Meir Meshulam, Christopher J. Honey, Rafael Malach, Qihong Lu, and Kenneth A. Norman for helpful discussion and comments on the manuscript. This work was supported by the National Institutes of Health under award numbers DP1HD091948 (U.H. and A.G.) and R01MH112566-01 (S.A.N.). Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",
year = "2020",
month = feb,
day = "5",
doi = "10.1016/j.neuron.2019.12.002",
language = "English (US)",
volume = "105",
pages = "416--434",
journal = "Neuron",
issn = "0896-6273",
publisher = "Cell Press",
number = "3",
}