World's Best Scientists 2026 revealed!

D-Index & Metrics

Neuroscience

D-Index
68
Citations
34723
World Ranking
2695
National Ranking
1262

Psychology

D-Index
68
Citations
34607
World Ranking
2425
National Ranking
1404

Overview

Randall C. O'Reilly is affiliated with the University of California, Davis, in the United States. Their primary field of study is neuroscience, with a particular focus on cognitive neuroscience. Their research spans multiple subfields, including artificial intelligence, cellular and molecular neuroscience, electrical and electronic engineering, and psychiatry and mental health. The main topics covered in their work include memory and neural mechanisms, neural dynamics and brain function, functional brain connectivity studies, neural and behavioral psychology studies, neuroscience and neuropharmacology research, neural networks and applications, and EEG and brain-computer interfaces.

O'Reilly has contributed to research published predominantly in venues such as arXiv (Cornell University) and bioRxiv (Cold Spring Harbor Laboratory). Other notable venues include Biological Psychiatry, Trends in Cognitive Sciences, and Psychological Review.

  • Deep Predictive Learning in Neocortex and Pulvinar (2021, PubMed Central)
  • Unraveling the Mysteries of Motivation (2020, Trends in Cognitive Sciences)
  • The Structure of Systematicity in the Brain (2022, Current Directions in Psychological Science)
  • A systems-neuroscience model of phasic dopamine (2020, Psychological Review)
  • Correcting the hebbian mistake: Toward a fully error-driven hippocampus (2022, PLoS Computational Biology)

Their frequent co-authors include Charan Ranganath, Jacob Russin, Yicong Zheng, Maryam Zolfaghar, and Xiaonan L. Liu.

Best Publications

  • Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

    James L. McClelland;Bruce L. McNaughton;Randall C. O'Reilly

  • By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism

    Michael J. Frank;Lauren C. Seeberger;Randall C. O'Reilly

  • Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach.

    Kenneth A. Norman;Randall C. O'Reilly

  • Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain

    Randall C. O'Reilly;Yuko Munakata

  • Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia

    Randall C. O'Reilly;Michael J. Frank

  • Hippocampal conjunctive encoding, storage, and recall: avoiding a trade-off.

    Randall C. O'Reilly;James L. McClelland

  • Interactions between frontal cortex and basal ganglia in working memory: a computational model.

    Michael J. Frank;Bryan Loughry;Randall C. O’Reilly

  • Conjunctive representations in learning and memory: principles of cortical and hippocampal function.

    Randall C. O'Reilly;Jerry W. Rudy

  • A unified framework for inhibitory control.

    Yuko Munakata;Seth A. Herd;Christopher H. Chatham;Brendan E. Depue

  • A computational approach to prefrontal cortex, cognitive control and schizophrenia: Recent developments and current challenges

    Jonathan D. Cohen;Todd S. Braver;Randall C. O'Reilly

  • Biologically Based Computational Models of High-Level Cognition

    Randall C. O'Reilly

  • A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol.

    Michael J. Frank;Randall C. O'Reilly

  • Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system

    Thomas E Hazy;Michael J Frank;Randall C O'Reilly

  • Hippocampal and neocortical contributions to memory: advances in the complementary learning systems framework

    Randall C O'Reilly;Kenneth A Norman

  • A Biologically Based Computational Model of Working Memory

    Randall C. O'Reilly;Todd S. Braver;Jonathan D. Cohen

  • Prefrontal cortex and flexible cognitive control: rules without symbols.

    Nicolas P. Rougier;David C. Noelle;Todd S. Braver;Jonathan D. Cohen

  • Six principles for biologically based computational models of cortical cognition

    Randall C. O'Reilly

  • Banishing the Homunculus: Making Working Memory Work

    T.E. Hazy;M.J. Frank;R.C. O’Reilly

  • Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm

    Randall C. O'Reilly

  • Under what conditions is recognition spared relative to recall after selective hippocampal damage in humans

    JS Holdstock;AR Mayes;Neil Roberts;E Cezayirli

Frequent Co-Authors

Jonathan D. Cohen
Jonathan D. Cohen Princeton University
Michael J. Frank
Michael J. Frank Brown University
Jerry W. Rudy
Jerry W. Rudy University of Colorado Boulder
Yuko Munakata
Yuko Munakata University of California, Davis
Tim Curran
Tim Curran University of Colorado Boulder
John R. Anderson
John R. Anderson Carnegie Mellon University
Shaun P. Vecera
Shaun P. Vecera University of Iowa
Todd S. Braver
Todd S. Braver Washington University in St. Louis
Kenneth A. Norman
Kenneth A. Norman Princeton University
Guido K.W. Frank
Guido K.W. Frank University of California, San Diego

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