Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Uri Hasson; Samuel A. Nastase; Ariel Goldstein

doi:10.1016/j.neuron.2019.12.002

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Uri Hasson, Samuel A. Nastase, Ariel Goldstein

Research output: Contribution to journal › Review article › peer-review

209 Scopus citations

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.

Original language	English (US)
Pages (from-to)	416-434
Number of pages	19
Journal	Neuron
Volume	105
Issue number	3
DOIs	https://doi.org/10.1016/j.neuron.2019.12.002
State	Published - Feb 5 2020

All Science Journal Classification (ASJC) codes

General Neuroscience

Keywords

evolution
experimental design
interpolation
learning
neural networks

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10.1016/j.neuron.2019.12.002

Cite this

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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.",

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author = "Uri Hasson and Nastase, {Samuel A.} and Ariel Goldstein",

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AU - Goldstein, Ariel

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KW - interpolation

KW - learning

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Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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