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Computer Science
Germany
2025

D-Index & Metrics

Computer Science

D-Index
104
Citations
88048
World Ranking
294
National Ranking
15

Peter Dayan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Peter Dayan sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 441 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Peter Dayan D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Peter Dayan sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 104 D-Index — 98th percentile

98% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award

Overview

Peter Dayan is affiliated with the Max Planck Institute for Biological Cybernetics in Germany. Their research primarily focuses on the field of Neuroscience, with significant contributions in Cognitive Neuroscience, Experimental and Cognitive Psychology, Artificial Intelligence, Social Psychology, and General Decision Sciences.

The main topics covered in their work include:

  • Neural and Behavioral Psychology Studies
  • Neural dynamics and brain function
  • Memory and Neural Mechanisms
  • Mental Health Research Topics
  • Decision-Making and Behavioral Economics
  • Functional Brain Connectivity Studies
  • Behavioral Health and Interventions

Peter Dayan has frequently published in several venues, with the most common being:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • PLoS Computational Biology
  • Trends in Cognitive Sciences
  • eLife

They have authored or coauthored papers with notable researchers including:

  • Chris Gagne
  • Raymond J. Dolan
  • Eric Schulz
  • Jonathan P. Roiser
  • Guido T. Meijer

Some recent publications include:

  • Space, Time, and Fear: Survival Computations along Defensive Circuits, 2020, Trends in Cognitive Sciences
  • The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection, 2020, Neuron
  • Impaired adaptation of learning to contingency volatility in internalizing psychopathology, 2020, eLife
  • Freezing revisited: coordinated autonomic and central optimization of threat coping, 2022, Nature Reviews Neuroscience
  • The value of what's to come: Neural mechanisms coupling prediction error and the utility of anticipation, 2020, Science Advances

In addition to journal articles, Dayan has contributed to book publications, including "For the Love of Art" (2022) published by the Modern Humanities Research Association.

Best Publications

  • A Neural Substrate of Prediction and Reward

    Schultz W;Dayan P;Montague Pr

  • Technical Note : \cal Q -Learning

    Christopher J. C. H. Watkins;Peter Dayan

  • Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

    Peter Dayan;L. F. Abbott

  • Technical Note Q-Learning

    Christopher J.C.H. Watkins;Peter Dayan

  • Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

    Nathaniel D Daw;Yael Niv;Yael Niv;Peter Dayan

  • A framework for mesencephalic dopamine systems based on predictive Hebbian learning

    PR Montague;P Dayan;TJ Sejnowski

  • Dissociable roles of ventral and dorsal striatum in instrumental conditioning

    John O'Doherty;Peter Dayan;Johannes Schultz;Ralf Deichmann

  • Uncertainty, neuromodulation, and attention.

    Angela J. Yu;Peter Dayan

  • Model-based influences on humans' choices and striatal prediction errors.

    Nathaniel D. Daw;Samuel J. Gershman;Ben Seymour;Peter Dayan

  • The helmholtz machine

    Peter Dayan;Geoffrey E. Hinton;Radford M. Neal;Richard S. Zemel

  • The "Wake-Sleep" Algorithm for Unsupervised Neural Networks

    Geoffrey E. Hinton;Peter Dayan;Brendan J. Frey;Radford M. Neal

  • States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning

    Jan Gläscher;Nathaniel Douglass Daw;Peter Dayan;John P. O'Doherty;John P. O'Doherty

  • Tonic dopamine: opportunity costs and the control of response vigor.

    Yael Niv;Yael Niv;Nathaniel D. Daw;Daphna Joel;Peter Dayan

  • Reward, Motivation, and Reinforcement Learning

    Peter Dayan;Bernard W. Balleine

  • The effect of correlated variability on the accuracy of a population code

    L. F. Abbott;Peter Dayan

  • Opponent interactions between serotonin and dopamine

    Nathaniel D. Daw;Sham Kakade;Peter Dayan

  • Information processing with population codes

    Alexandre Pouget;Peter Dayan;Richard Zemel

  • Improving generalization for temporal difference learning: The successor representation

    Peter Dayan

  • Reinforcement learning: The Good, The Bad and The Ugly

    Peter Dayan;Yael Niv

  • Feudal Reinforcement Learning

    Peter Dayan;Geoffrey E. Hinton

  • Adaptation and Unsupervised Learning

    Peter Dayan;Maneesh Sahani;Gregoire Deback

Frequent Co-Authors

Raymond J. Dolan
Raymond J. Dolan University College London
Quentin J. M. Huys
Quentin J. M. Huys University College London
Nathaniel D. Daw
Nathaniel D. Daw Princeton University
Jonathan P. Roiser
Jonathan P. Roiser University College London
Ben Seymour
Ben Seymour University of Oxford
Emrah Düzel
Emrah Düzel German Center for Neurodegenerative Diseases
P. Read Montague
P. Read Montague Virginia Tech
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Marc Guitart-Masip
Marc Guitart-Masip Karolinska Institute
Richard S. Zemel
Richard S. Zemel University of Toronto

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