World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
7015
World Ranking
9706
National Ranking
141

Jun Tani 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 Jun Tani 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: 260 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Jun Tani 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 Jun Tani 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Overview

Jun Tani is affiliated with the Okinawa Institute of Science and Technology in Japan. Their research spans multiple fields, primarily Neuroscience and Computer Science, with notable focus on Cognitive Neuroscience and Artificial Intelligence as subfields.

Their work addresses various topics including Embodied and Extended Cognition, Neural dynamics and brain function, Action Observation and Synchronization, Reinforcement Learning in Robotics, Robot Manipulation and Learning, Neural Networks and Applications, and EEG and Brain-Computer Interfaces.

Recent scholarly contributions by Jun Tani include the following papers:

  • "Active Inference in Robotics and Artificial Agents: Survey and Challenges," 2021, published in arXiv (Cornell University)
  • "Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network," 2020, published in Entropy
  • "Emergence of sensory attenuation based upon the free-energy principle," 2022, published in Scientific Reports
  • "Cognitive neurorobotics and self in the shared world, a focused review of ongoing research," 2020, published in Adaptive Behavior
  • "Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks," 2020, published in Neural Networks

Frequent co-authors of Jun Tani include:

  • Wataru Ohata
  • Takazumi Matsumoto
  • Yuichi Yamashita
  • Kenji Doya
  • Jeffrey Queißer

Publication venues where Jun Tani frequently contributes are:

  • arXiv (Cornell University)
  • Entropy
  • Neural Computation
  • bioRxiv (Cold Spring Harbor Laboratory)
  • npj Complexity

Best Publications

  • Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment

    Yuichi Yamashita;Jun Tani

  • Model-based learning for mobile robot navigation from the dynamical systems perspective

    J. Tani

  • Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems

    J. Tani;S. Nolfi

  • Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB

    Jun Tani;Masato Ito;Yuuya Sugita

  • Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment

    J. Tani;M. Ito

  • Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes

    Yuuya Sugita;Jun Tani

  • Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

    A Cangelosi;G Metta;G Sagerer;S Nolfi

  • Learning to generate articulated behavior through the bottom-up and the top-down interaction processes

    Jun Tani

  • Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization.

    German Ignacio Parisi;Jun Tani;Cornelius Weber;Stefan Wermter

  • 2006 Special issue: Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model

    Masato Ito;Kuniaki Noda;Yukiko Hoshino;Jun Tani

  • Lifelong learning of human actions with deep neural network self-organization

    German Ignacio Parisi;Jun Tani;Cornelius Weber;Stefan Wermter

  • On-line Imitative Interaction with a Humanoid Robot Using a Dynamic Neural Network Model of a Mirror System:

    Masato Ito;Jun Tani

  • An Interpretation of the "Self" From the Dynamical Systems Perspective: A Constructivist Approach

    Jun Tani

  • How Hierarchical Control Self-organizes in Artificial Adaptive Systems:

    Rainer W. Paine;Jun Tani

  • Extracting Regularities in Space and Time Through a Cascade of Prediction Networks: The Case of a Mobile Robot Navigating in a Structured Environment

    Stefano Nolfi;Jun Tani

  • Learning goal-directed sensory-based navigation of a mobile robot

    Jun Tani;Naohiro Fukumura

  • Learning to Reproduce Fluctuating Time Series by Inferring Their Time-Dependent Stochastic Properties: Application in Robot Learning Via Tutoring

    Shingo Murata;Jun Namikawa;Hiroaki Arie;Shigeki Sugano

  • Motor primitive and sequence self-organization in a hierarchical recurrent neural network

    Rainer W. Paine;Jun Tani

  • A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition

    Ahmadreza Ahmadi;Jun Tani

  • Two-way translation of compound sentences and arm motions by recurrent neural networks

    T. Ogata;M. Murase;Jun Tani;K. Komatani

  • Self-organization of behavioral primitives as multiple attractor dynamics: a robot experiment

    Jun Tani

Frequent Co-Authors

Tetsuya Ogata
Tetsuya Ogata Waseda University
Hiroshi G. Okuno
Hiroshi G. Okuno Waseda University
Shigeki Sugano
Shigeki Sugano Waseda University
Kazuo Okanoya
Kazuo Okanoya University of Tokyo
Keiji Tanaka
Keiji Tanaka RIKEN Center for Brain Science
Angelo Cangelosi
Angelo Cangelosi University of Manchester
Stefano Nolfi
Stefano Nolfi National Research Council (CNR)
Giorgio Metta
Giorgio Metta Italian Institute of Technology
Katharina J. Rohlfing
Katharina J. Rohlfing University of Paderborn
Kenji Doya
Kenji Doya Okinawa Institute of Science and Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

If you’re considering studying Computer Science in the USA, it’s worth exploring related online degrees that can accelerate your entry into a tech-driven career. Many students seek quick degrees online that pay well to gain marketable skills efficiently and start earning sooner.

Specialized programs such as an artificial intelligence degree online are increasingly popular. These degrees offer a gateway into fields like AI and data science, which are among the best college degrees for the future. Graduates are equipped for in-demand roles in tech, finance, healthcare, and more.

If you’re aiming to enhance your credentials, enrolling in the easiest online masters degree programs can provide career advancement opportunities with flexible schedules. By combining core Computer Science education with related online degrees, you can create a personalized learning path tailored to your goals and the evolving technology job market.

Best Scientists Citing Jun Tani

Trending Scientists