Associative Learning Should Go Deep

Esther Mondragón

doi:10.1016/j.tics.2017.06.001

2017-11-01

Esther Mondragón

Eduardo Alonso

Niklas H. Kokkola *

DOI：10.1016/j.tics.2017.06.001

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摘要

En 中文

Conditioning, how animals learn to associate two or more events, is one of the most influential paradigms in learning theory. It is nevertheless unclear how current models of associative learning can accommodate complex phenomena without ad hoc representational assumptions. We propose to embrace deep neural networks to negotiate this problem.

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期刊

Trends in Cognitive Sciences

IF:

17.2

论文数:

3.6K

被引数:

3.5W

机构

university of london

学者数:

21.5W

论文数: 19.7W

被引数: 305

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