Prefrontal cortex as a meta-reinforcement learning system
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Joel Z. Leibo | Jane X. Wang | D. Hassabis | D. Kumaran | Dhruva Tirumala | M. Botvinick | Z. Kurth-Nelson | Hubert Soyer
[1] H. Harlow,et al. The formation of learning sets. , 1949, Psychological review.
[2] W M Baum,et al. Optimization and the matching law as accounts of instrumental behavior. , 1981, Journal of the experimental analysis of behavior.
[3] Joel L. Davis,et al. A Model of How the Basal Ganglia Generate and Use Neural Signals That Predict Reinforcement , 1994 .
[4] P. Dayan,et al. A framework for mesencephalic dopamine systems based on predictive Hebbian learning , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[5] Jieyu Zhao,et al. Simple Principles of Metalearning , 1996 .
[6] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[7] J. Tanji,et al. Role for cingulate motor area cells in voluntary movement selection based on reward. , 1998, Science.
[8] Sebastian Thrun,et al. Learning to Learn , 1998, Springer US.
[9] M. Shadlen,et al. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque , 1999, Nature Neuroscience.
[10] Xiao-Jing Wang. Synaptic reverberation underlying mnemonic persistent activity , 2001, Trends in Neurosciences.
[11] Junichiro Yoshimoto,et al. Control of exploitation-exploration meta-parameter in reinforcement learning , 2002, Neural Networks.
[12] Kenji Doya,et al. Meta-learning in Reinforcement Learning , 2003, Neural Networks.
[13] Michael J. Frank,et al. By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism , 2004, Science.
[14] D. Barraclough,et al. Prefrontal cortex and decision making in a mixed-strategy game , 2004, Nature Neuroscience.
[15] Jonathan D. Cohen,et al. Prefrontal cortex and flexible cognitive control: rules without symbols. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[16] P. Dayan,et al. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control , 2005, Nature Neuroscience.
[17] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[18] P. Glimcher,et al. JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR 2005, 84, 555–579 NUMBER 3(NOVEMBER) DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS , 2022 .
[19] C. Padoa-Schioppa,et al. Neurons in the orbitofrontal cortex encode economic value , 2006, Nature.
[20] Michael J. Frank,et al. Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia , 2006, Neural Computation.
[21] J. O'Doherty,et al. The Role of the Ventromedial Prefrontal Cortex in Abstract State-Based Inference during Decision Making in Humans , 2006, The Journal of Neuroscience.
[22] Keiji Tanaka,et al. Medial prefrontal cell activity signaling prediction errors of action values , 2007, Nature Neuroscience.
[23] Jadin C. Jackson,et al. Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling. , 2007, Psychological review.
[24] Timothy E. J. Behrens,et al. Learning the value of information in an uncertain world , 2007, Nature Neuroscience.
[25] H. Seo,et al. Dynamic signals related to choices and outcomes in the dorsolateral prefrontal cortex. , 2007, Cerebral cortex.
[26] Paul R. Schrater,et al. Structure Learning in Human Sequential Decision-Making , 2008, NIPS.
[27] A. Graybiel. Habits, rituals, and the evaluative brain. , 2008, Annual review of neuroscience.
[28] Timothy E. J. Behrens,et al. Choice, uncertainty and value in prefrontal and cingulate cortex , 2008, Nature Neuroscience.
[29] H. Seo,et al. Cortical mechanisms for reinforcement learning in competitive games , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.
[30] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[31] S. Kennerley,et al. Evaluating choices by single neurons in the frontal lobe: outcome value encoded across multiple decision variables , 2009, The European journal of neuroscience.
[32] Jia Deng,et al. A large-scale hierarchical image database , 2009, CVPR 2009.
[33] Simon Hong,et al. A pallidus-habenula-dopamine pathway signals inferred stimulus values. , 2010, Journal of neurophysiology.
[34] Robert C. Wilson,et al. An Approximately Bayesian Delta-Rule Model Explains the Dynamics of Belief Updating in a Changing Environment , 2010, The Journal of Neuroscience.
[35] P. Glimcher. Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis , 2011, Proceedings of the National Academy of Sciences.
[36] P. Dayan,et al. Model-based influences on humans’ choices and striatal prediction errors , 2011, Neuron.
[37] Robert C. Wilson,et al. Expectancy-related changes in firing of dopamine neurons depend on orbitofrontal cortex , 2011, Nature Neuroscience.
[38] O. Hikosaka,et al. Learning to represent reward structure: A key to adapting to complex environments , 2012, Neuroscience Research.
[39] B. Averbeck,et al. Action Selection and Action Value in Frontal-Striatal Circuits , 2012, Neuron.
[40] Anne G E Collins,et al. How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis , 2012, The European journal of neuroscience.
[41] C. Fiorillo,et al. Optogenetic Mimicry of the Transient Activation of Dopamine Neurons by Natural Reward Is Sufficient for Operant Reinforcement , 2012, PloS one.
[42] Navdeep Jaitly,et al. Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.
[43] Daeyeol Lee,et al. Signals for Previous Goal Choice Persist in the Dorsomedial, but Not Dorsolateral Striatum of Rats , 2013, The Journal of Neuroscience.
[44] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[45] P. Dayan,et al. Goals and Habits in the Brain , 2013, Neuron.
[46] Josiah R. Boivin,et al. A Causal Link Between Prediction Errors, Dopamine Neurons and Learning , 2013, Nature Neuroscience.
[47] Peter Ford Dominey,et al. Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters. , 2013, Progress in brain research.
[48] Etienne Koechlin,et al. Foundations of human reasoning in the prefrontal cortex , 2014, Science.
[49] Robert C. Wilson,et al. Orbitofrontal Cortex as a Cognitive Map of Task Space , 2014, Neuron.
[50] S. Floresco,et al. Overriding Phasic Dopamine Signals Redirects Action Selection during Risk/Reward Decision Making , 2014, Neuron.
[51] S. Haber. The place of dopamine in the cortico-basal ganglia circuit , 2014, Neuroscience.
[52] Christopher H Chatham,et al. Multiple gates on working memory , 2015, Current Opinion in Behavioral Sciences.
[53] Peter Dayan,et al. Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task , 2015, bioRxiv.
[54] Y. Niv,et al. Discovering latent causes in reinforcement learning , 2015, Current Opinion in Behavioral Sciences.
[55] Alec Solway,et al. Reinforcement learning, efficient coding, and the statistics of natural tasks , 2015, Current Opinion in Behavioral Sciences.
[56] Ilana B. Witten,et al. Reward and choice encoding in terminals of midbrain dopamine neurons depends on striatal target , 2016, Nature Neuroscience.
[57] Yael Niv,et al. A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex , 2016, The Journal of Neuroscience.
[58] Nicolas W. Schuck,et al. Human Orbitofrontal Cortex Represents a Cognitive Map of State Space , 2016, Neuron.
[59] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[60] M. Frank,et al. Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning , 2016, Cognition.
[61] Guillem R. Esber,et al. Brief optogenetic inhibition of dopamine neurons mimics endogenous negative reward prediction errors , 2015, Nature Neuroscience.
[62] Sergio Gomez Colmenarejo,et al. Hybrid computing using a neural network with dynamic external memory , 2016, Nature.
[63] Geoffrey Schoenbaum,et al. Midbrain dopamine neurons compute inferred and cached value prediction errors in a common framework , 2016, eLife.
[64] Wolfram Schultz,et al. Dopamine reward prediction-error signalling: a two-component response , 2016, Nature Reviews Neuroscience.
[65] Fabian Grabenhorst,et al. A dynamic code for economic object valuation in prefrontal cortex neurons , 2016, Nature Communications.
[66] Peter L. Bartlett,et al. RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning , 2016, ArXiv.
[67] Kiyohito Iigaya,et al. Adaptive learning and decision-making under uncertainty by metaplastic synapses guided by a surprise detection system , 2016, eLife.
[68] Peter Ford Dominey,et al. Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex , 2016, PLoS Comput. Biol..
[69] S. Gershman,et al. Dopamine reward prediction errors reflect hidden state inference across time , 2017, Nature Neuroscience.
[70] Kevin J. Miller,et al. Dorsal hippocampus contributes to model-based planning , 2017, Nature Neuroscience.
[71] Zeb Kurth-Nelson,et al. Learning to reinforcement learn , 2016, CogSci.
[72] Xiao-Jing Wang,et al. Reward-based training of recurrent neural networks for cognitive and value-based tasks , 2016, bioRxiv.
[73] N. Daw,et al. Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework , 2017, Annual review of psychology.
[74] Shane Legg,et al. Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents , 2018, ArXiv.
[75] Horatio M. Morgan,et al. An Integrative Framework , 2019, Underdog Entrepreneurs.