Meta-learning in natural and artificial intelligence

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

(2020) Uri Hasson et al.   NEURON

Deep Reinforcement Learning and Its Neuroscientific Implications

(2020) Matthew Botvinick et al.   NEURON

A solution to the learning dilemma for recurrent networks of spiking neurons

(2020) Guillaume Bellec et al.   Nature Communications

Reinforcement Learning, Fast and Slow

(2019) Matthew Botvinick et al.   TRENDS IN COGNITIVE SCIENCES

A deep learning framework for neuroscience

(2019) Blake A. Richards et al.   NATURE NEUROSCIENCE

A critique of pure learning and what artificial neural networks can learn from animal brains

(2019) Anthony M. Zador   Nature Communications

Doing more with less: meta-reasoning and meta-learning in humans and machines

(2019) Thomas L Griffiths et al.   Current Opinion in Behavioral Sciences

Prefrontal cortex as a meta-reinforcement learning system

(2018) Jane X. Wang et al.   NATURE NEUROSCIENCE

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy

(2018) Alexander J.E. Kell et al.   NEURON

What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior

(2018) Timothy E.J. Behrens et al.   NEURON

Neuroscience-Inspired Artificial Intelligence

(2017) Demis Hassabis et al.   NEURON

Building machines that learn and think like people

(2016) Brenden M. Lake et al.   BEHAVIORAL AND BRAIN SCIENCES

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Using goal-driven deep learning models to understand sensory cortex

(2016) Daniel L K Yamins et al.   NATURE NEUROSCIENCE

Organizing conceptual knowledge in humans with a gridlike code

(2016) A. O. Constantinescu et al.   SCIENCE

Toward an Integration of Deep Learning and Neuroscience

(2016) Adam H. Marblestone et al.   Frontiers in Computational Neuroscience

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Human-level concept learning through probabilistic program induction

(2015) B. M. Lake et al.   SCIENCE

Neural Computations Underlying Arbitration between Model-Based and Model-free Learning

(2014) Sang Wan Lee et al.   NEURON

Foundations of human reasoning in the prefrontal cortex

(2014) M. Donoso et al.   SCIENCE

Optimal Behavioral Hierarchy

(2014) Alec Solway et al.   PLoS Computational Biology

Cognitive control over learning: Creating, clustering, and generalizing task-set structure.

(2013) Anne G. E. Collins et al.   PSYCHOLOGICAL REVIEW

Learning to represent reward structure: A key to adapting to complex environments

(2012) Hiroyuki Nakahara et al.   NEUROSCIENCE RESEARCH

How schema and novelty augment memory formation

(2012) Marlieke T.R. van Kesteren et al.   TRENDS IN NEUROSCIENCES

Learning latent structure: carving nature at its joints

(2010) Samuel J Gershman et al.   CURRENT OPINION IN NEUROBIOLOGY

Frontal Cortex and the Discovery of Abstract Action Rules

(2010) David Badre et al.   NEURON

Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes

(2008) David Badre   TRENDS IN COGNITIVE SCIENCES