A citation-based method for searching scientific literature

Angela J Langdon, Melissa J Sharpe, Geoffrey Schoenbaum, Yael Niv. Curr Opin Neurobiol 2018
Times Cited: 43







List of co-cited articles
470 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
51

Dopamine transients are sufficient and necessary for acquisition of model-based associations.
Melissa J Sharpe, Chun Yun Chang, Melissa A Liu, Hannah M Batchelor, Lauren E Mueller, Joshua L Jones, Yael Niv, Geoffrey Schoenbaum. Nat Neurosci 2017
88
37

Dopamine Neurons Respond to Errors in the Prediction of Sensory Features of Expected Rewards.
Yuji K Takahashi, Hannah M Batchelor, Bing Liu, Akash Khanna, Marisela Morales, Geoffrey Schoenbaum. Neuron 2017
71
32

Orbitofrontal cortex as a cognitive map of task space.
Robert C Wilson, Yuji K Takahashi, G Schoenbaum, Yael Niv. Neuron 2014
380
27


Dopamine reward prediction errors reflect hidden-state inference across time.
Clara Kwon Starkweather, Benedicte M Babayan, Naoshige Uchida, Samuel J Gershman. Nat Neurosci 2017
60
23



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
694
20






Optogenetic Blockade of Dopamine Transients Prevents Learning Induced by Changes in Reward Features.
Chun Yun Chang, Matthew Gardner, Maria Gonzalez Di Tillio, Geoffrey Schoenbaum. Curr Biol 2017
31
22

Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons.
Ju Tian, Ryan Huang, Jeremiah Y Cohen, Fumitaka Osakada, Dmitry Kobak, Christian K Machens, Edward M Callaway, Naoshige Uchida, Mitsuko Watabe-Uchida. Neuron 2016
93
16

Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments.
Yuan Chang Leong, Angela Radulescu, Reka Daniel, Vivian DeWoskin, Yael Niv. Neuron 2017
97
16

Human Orbitofrontal Cortex Represents a Cognitive Map of State Space.
Nicolas W Schuck, Ming Bo Cai, Robert C Wilson, Yael Niv. Neuron 2016
176
16

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
939
16

Dopamine neurons encode the better option in rats deciding between differently delayed or sized rewards.
Matthew R Roesch, Donna J Calu, Geoffrey Schoenbaum. Nat Neurosci 2007
374
13

Dopamine in motivational control: rewarding, aversive, and alerting.
Ethan S Bromberg-Martin, Masayuki Matsumoto, Okihide Hikosaka. Neuron 2010
13

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
431
13

Phasic dopamine release in the rat nucleus accumbens symmetrically encodes a reward prediction error term.
Andrew S Hart, Robb B Rutledge, Paul W Glimcher, Paul E M Phillips. J Neurosci 2014
147
13

Dichotomous dopaminergic control of striatal synaptic plasticity.
Weixing Shen, Marc Flajolet, Paul Greengard, D James Surmeier. Science 2008
731
13

Ventral tegmental area: cellular heterogeneity, connectivity and behaviour.
Marisela Morales, Elyssa B Margolis. Nat Rev Neurosci 2017
413
13

Arithmetic and local circuitry underlying dopamine prediction errors.
Neir Eshel, Michael Bukwich, Vinod Rao, Vivian Hemmelder, Ju Tian, Naoshige Uchida. Nature 2015
161
13



Cortical substrates for exploratory decisions in humans.
Nathaniel D Daw, John P O'Doherty, Peter Dayan, Ben Seymour, Raymond J Dolan. Nature 2006
13

Ventral Tegmental Dopamine Neurons Participate in Reward Identity Predictions.
Ronald Keiflin, Heather J Pribut, Nisha B Shah, Patricia H Janak. Curr Biol 2019
30
20


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
105
13

The ubiquity of model-based reinforcement learning.
Bradley B Doll, Dylan A Simon, Nathaniel D Daw. Curr Opin Neurobiol 2012
150
13

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
51
13

Action initiation shapes mesolimbic dopamine encoding of future rewards.
Emilie C J Syed, Laura L Grima, Peter J Magill, Rafal Bogacz, Peter Brown, Mark E Walton. Nat Neurosci 2016
97
11


Mesolimbic dopamine signals the value of work.
Arif A Hamid, Jeffrey R Pettibone, Omar S Mabrouk, Vaughn L Hetrick, Robert Schmidt, Caitlin M Vander Weele, Robert T Kennedy, Brandon J Aragona, Joshua D Berke. Nat Neurosci 2016
310
11


Dopamine: generalization and bonuses.
Sham Kakade, Peter Dayan. Neural Netw 2002
210
11



Dopamine neurons create Pavlovian conditioned stimuli with circuit-defined motivational properties.
Benjamin T Saunders, Jocelyn M Richard, Elyssa B Margolis, Patricia H Janak. Nat Neurosci 2018
116
11

A pallidus-habenula-dopamine pathway signals inferred stimulus values.
Ethan S Bromberg-Martin, Masayuki Matsumoto, Simon Hong, Okihide Hikosaka. J Neurophysiol 2010
102
11

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
75
11


The hippocampus as a predictive map.
Kimberly L Stachenfeld, Matthew M Botvinick, Samuel J Gershman. Nat Neurosci 2017
184
11


Uncertainty, neuromodulation, and attention.
Angela J Yu, Peter Dayan. Neuron 2005
830
11

By carrot or by stick: cognitive reinforcement learning in parkinsonism.
Michael J Frank, Lauren C Seeberger, Randall C O'reilly. Science 2004
11

Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons.
Ben Engelhard, Joel Finkelstein, Julia Cox, Weston Fleming, Hee Jae Jang, Sharon Ornelas, Sue Ann Koay, Stephan Y Thiberge, Nathaniel D Daw, David W Tank,[...]. Nature 2019
105
11


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.