Associative Learning Should Go Deep.

Journal: Trends in cognitive sciences
PMID: 28668210

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.

Authors

  • Esther Mondragón
    Research Centre for Systems and Control, University of London, London EC1V 0HB, UK; Computational and Animal Learning Research Centre, St Albans AL1 1RQ, UK. Electronic address: e.mondragon@cal-r.org.
  • Eduardo Alonso
  • Niklas Kokkola
    Research Centre for Systems and Control, University of London, London EC1V 0HB, UK; Computational and Animal Learning Research Centre, St Albans AL1 1RQ, UK. Electronic address: Niklas.Kokkola.1@city.ac.uk.

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