City Research Online

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:
[thumbnail of MondragonTICSCity.pdf]
Preview
Text - Accepted Version
Download (1MB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login