Format

Send to

Choose Destination
Nat Neurosci. 2018 Jun;21(6):860-868. doi: 10.1038/s41593-018-0147-8. Epub 2018 May 14.

Prefrontal cortex as a meta-reinforcement learning system.

Author information

1
DeepMind, London, UK.
2
Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
3
Institute of Cognitive Neuroscience, University College London, London, UK.
4
Gatsby Computational Neuroscience Unit, University College London, London, UK.
5
DeepMind, London, UK. botvinick@google.com.
6
Gatsby Computational Neuroscience Unit, University College London, London, UK. botvinick@google.com.

Abstract

Over the past 20 years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine 'stamps in' associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. We now draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.

PMID:
29760527
DOI:
10.1038/s41593-018-0147-8

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center