Associative Learning Should Go Deep
Mondragon, E. 
ORCID: 0000-0003-4180-1261, Alonso, E. & Kokkola, N. (2017).
Associative Learning Should Go Deep.
Trends in Cognitive Science, 21(11),
pp. 822-825.
doi: 10.1016/j.tics.2017.06.001
Abstract
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.
| Publication Type: | Article |
|---|---|
| Publisher Keywords: | associative learning, deep neural networks |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | School of Science & Technology > Computer Science |
| SWORD Depositor: |
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