Literature DB >> 29760527

Prefrontal cortex as a meta-reinforcement learning system.

Jane X Wang1, Zeb Kurth-Nelson1,2, Dharshan Kumaran1,3, Dhruva Tirumala1, Hubert Soyer1, Joel Z Leibo1, Demis Hassabis1,4, Matthew Botvinick5,6.   

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.

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Year:  2018        PMID: 29760527     DOI: 10.1038/s41593-018-0147-8

Source DB:  PubMed          Journal:  Nat Neurosci        ISSN: 1097-6256            Impact factor:   24.884


  78 in total

1.  Medial Prefrontal Cortex Population Activity Is Plastic Irrespective of Learning.

Authors:  Abhinav Singh; Adrien Peyrache; Mark D Humphries
Journal:  J Neurosci       Date:  2019-02-27       Impact factor: 6.167

2.  News Feature: What are the limits of deep learning?

Authors:  M Mitchell Waldrop
Journal:  Proc Natl Acad Sci U S A       Date:  2019-01-22       Impact factor: 11.205

3.  How to study the neural mechanisms of multiple tasks.

Authors:  Guangyu Robert Yang; Michael W Cole; Kanaka Rajan
Journal:  Curr Opin Behav Sci       Date:  2019-09-09

Review 4.  Reevaluating the Role of Persistent Neural Activity in Short-Term Memory.

Authors:  Nicolas Y Masse; Matthew C Rosen; David J Freedman
Journal:  Trends Cogn Sci       Date:  2020-01-29       Impact factor: 20.229

5.  A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex.

Authors:  Ben Tsuda; Kay M Tye; Hava T Siegelmann; Terrence J Sejnowski
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-05       Impact factor: 11.205

6.  Computational evidence for hierarchically structured reinforcement learning in humans.

Authors:  Maria K Eckstein; Anne G E Collins
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-24       Impact factor: 11.205

7.  Simple framework for constructing functional spiking recurrent neural networks.

Authors:  Robert Kim; Yinghao Li; Terrence J Sejnowski
Journal:  Proc Natl Acad Sci U S A       Date:  2019-10-21       Impact factor: 11.205

8.  Catecholaminergic modulation of meta-learning.

Authors:  Hanneke Em den Ouden; Roshan Cools; Jennifer L Cook; Jennifer C Swart; Monja I Froböse; Andreea O Diaconescu; Dirk Em Geurts
Journal:  Elife       Date:  2019-12-18       Impact factor: 8.140

9.  Stable Representations of Decision Variables for Flexible Behavior.

Authors:  Bilal A Bari; Cooper D Grossman; Emily E Lubin; Adithya E Rajagopalan; Jianna I Cressy; Jeremiah Y Cohen
Journal:  Neuron       Date:  2019-07-04       Impact factor: 17.173

10.  Adaptive Regulation of Motor Variability.

Authors:  Ashesh K Dhawale; Yohsuke R Miyamoto; Maurice A Smith; Bence P Ölveczky
Journal:  Curr Biol       Date:  2019-10-17       Impact factor: 10.834

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