A citation-based method for searching scientific literature

Prefrontal cortex as a meta-reinforcement learning system.
Jane X Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Demis Hassabis, Matthew Botvinick. Nat Neurosci 2018
Times Cited: 129






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List of co-cited articles
1055 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


A neural substrate of prediction and reward.
W Schultz, P Dayan, P R Montague. Science 1997
4596
33
33

Human-level control through deep reinforcement learning.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski,[...]. Nature 2015
959
21
21

Building machines that learn and think like people.
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman. Behav Brain Sci 2017
200
20
20

Reinforcement Learning, Fast and Slow.
Matthew Botvinick, Sam Ritter, Jane X Wang, Zeb Kurth-Nelson, Charles Blundell, Demis Hassabis. Trends Cogn Sci 2019
74
19
25

Model-based influences on humans' choices and striatal prediction errors.
Nathaniel D Daw, Samuel J Gershman, Ben Seymour, Peter Dayan, Raymond J Dolan. Neuron 2011
741
18
18

The hippocampus as a predictive map.
Kimberly L Stachenfeld, Matthew M Botvinick, Samuel J Gershman. Nat Neurosci 2017
213
18
18

What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior.
Timothy E J Behrens, Timothy H Muller, James C R Whittington, Shirley Mark, Alon B Baram, Kimberly L Stachenfeld, Zeb Kurth-Nelson. Neuron 2018
197
16
16

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control.
Nathaniel D Daw, Yael Niv, Peter Dayan. Nat Neurosci 2005
1159
14
14

Task representations in neural networks trained to perform many cognitive tasks.
Guangyu Robert Yang, Madhura R Joglekar, H Francis Song, William T Newsome, Xiao-Jing Wang. Nat Neurosci 2019
83
14
16

Vector-based navigation using grid-like representations in artificial agents.
Andrea Banino, Caswell Barry, Benigno Uria, Charles Blundell, Timothy Lillicrap, Piotr Mirowski, Alexander Pritzel, Martin J Chadwick, Thomas Degris, Joseph Modayil,[...]. Nature 2018
114
14
14

Context-dependent computation by recurrent dynamics in prefrontal cortex.
Valerio Mante, David Sussillo, Krishna V Shenoy, William T Newsome. Nature 2013
609
14
14

A distributional code for value in dopamine-based reinforcement learning.
Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew Botvinick. Nature 2020
70
14
20

Mastering the game of Go with deep neural networks and tree search.
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot,[...]. Nature 2016
1026
13
13

States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.
Jan Gläscher, Nathaniel Daw, Peter Dayan, John P O'Doherty. Neuron 2010
577
13
13

Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective.
Matthew M Botvinick, Yael Niv, Andew G Barto. Cognition 2009
242
12
12

Reward-based training of recurrent neural networks for cognitive and value-based tasks.
H Francis Song, Guangyu R Yang, Xiao-Jing Wang. Elife 2017
52
12
23

Orbitofrontal cortex as a cognitive map of task space.
Robert C Wilson, Yuji K Takahashi, G Schoenbaum, Yael Niv. Neuron 2014
399
12
12

Learning task-state representations.
Yael Niv. Nat Neurosci 2019
67
12
17

Neuroscience-Inspired Artificial Intelligence.
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick. Neuron 2017
218
11
11

A deep learning framework for neuroscience.
Blake A Richards, Timothy P Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, Rui Ponte Costa, Archy de Berker, Surya Ganguli,[...]. Nat Neurosci 2019
160
11
11

Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework.
Samuel J Gershman, Nathaniel D Daw. Annu Rev Psychol 2017
116
11
11

Learning the value of information in an uncertain world.
Timothy E J Behrens, Mark W Woolrich, Mark E Walton, Matthew F S Rushworth. Nat Neurosci 2007
984
11
11

The formation of learning sets.
H F HARLOW. Psychol Rev 1949
782
11
11

Representation of action-specific reward values in the striatum.
Kazuyuki Samejima, Yasumasa Ueda, Kenji Doya, Minoru Kimura. Science 2005
565
11
11

Human Orbitofrontal Cortex Represents a Cognitive Map of State Space.
Nicolas W Schuck, Ming Bo Cai, Robert C Wilson, Yael Niv. Neuron 2016
192
11
11

Midbrain dopamine neurons encode a quantitative reward prediction error signal.
Hannah M Bayer, Paul W Glimcher. Neuron 2005
699
11
11

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
392
11
11

Organizing conceptual knowledge in humans with a gridlike code.
Alexandra O Constantinescu, Jill X O'Reilly, Timothy E J Behrens. Science 2016
260
11
11

Cognitive maps in rats and men.
E C TOLMAN. Psychol Rev 1948
1586
11
11

Deep Reinforcement Learning and Its Neuroscientific Implications.
Matthew Botvinick, Jane X Wang, Will Dabney, Kevin J Miller, Zeb Kurth-Nelson. Neuron 2020
26
11
42

Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia.
Randall C O'Reilly, Michael J Frank. Neural Comput 2006
494
10
10

Neural Circuitry of Reward Prediction Error.
Mitsuko Watabe-Uchida, Neir Eshel, Naoshige Uchida. Annu Rev Neurosci 2017
117
10
10

How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis.
Anne G E Collins, Michael J Frank. Eur J Neurosci 2012
162
10
10

Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments.
Yuan Chang Leong, Angela Radulescu, Reka Daniel, Vivian DeWoskin, Yael Niv. Neuron 2017
107
10
10

Long short-term memory.
S Hochreiter, J Schmidhuber. Neural Comput 1997
3838
9
9

Performance-optimized hierarchical models predict neural responses in higher visual cortex.
Daniel L K Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, James J DiCarlo. Proc Natl Acad Sci U S A 2014
530
9
9

Goals and habits in the brain.
Ray J Dolan, Peter Dayan. Neuron 2013
432
9
9

A causal link between prediction errors, dopamine neurons and learning.
Elizabeth E Steinberg, Ronald Keiflin, Josiah R Boivin, Ilana B Witten, Karl Deisseroth, Patricia H Janak. Nat Neurosci 2013
456
9
9

Neural basis of reinforcement learning and decision making.
Daeyeol Lee, Hyojung Seo, Min Whan Jung. Annu Rev Neurosci 2012
199
9
9

Backpropagation and the brain.
Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, Geoffrey Hinton. Nat Rev Neurosci 2020
90
9
10

The successor representation in human reinforcement learning.
I Momennejad, E M Russek, J H Cheong, M M Botvinick, N D Daw, S J Gershman. Nat Hum Behav 2017
92
9
9

By carrot or by stick: cognitive reinforcement learning in parkinsonism.
Michael J Frank, Lauren C Seeberger, Randall C O'reilly. Science 2004
1203
8
8

Neuron-type-specific signals for reward and punishment in the ventral tegmental area.
Jeremiah Y Cohen, Sebastian Haesler, Linh Vong, Bradford B Lowell, Naoshige Uchida. Nature 2012
713
8
8

Dorsal hippocampus contributes to model-based planning.
Kevin J Miller, Matthew M Botvinick, Carlos D Brody. Nat Neurosci 2017
74
8
10

Hippocampal Contributions to Model-Based Planning and Spatial Memory.
Oliver M Vikbladh, Michael R Meager, John King, Karen Blackmon, Orrin Devinsky, Daphna Shohamy, Neil Burgess, Nathaniel D Daw. Neuron 2019
45
8
17

Predictive representations can link model-based reinforcement learning to model-free mechanisms.
Evan M Russek, Ida Momennejad, Matthew M Botvinick, Samuel J Gershman, Nathaniel D Daw. PLoS Comput Biol 2017
85
8
9

Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits 2: evidence from fMRI.
David Badre, Michael J Frank. Cereb Cortex 2012
113
8
8

Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis.
Michael J Frank, David Badre. Cereb Cortex 2012
159
8
8

Dissociable roles of ventral and dorsal striatum in instrumental conditioning.
John O'Doherty, Peter Dayan, Johannes Schultz, Ralf Deichmann, Karl Friston, Raymond J Dolan. Science 2004
1346
8
8

Cognitive control over learning: creating, clustering, and generalizing task-set structure.
Anne G E Collins, Michael J Frank. Psychol Rev 2013
162
8
8


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.

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