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Computer Science

D-Index
39
Citations
7015
World Ranking
9707
National Ranking
141

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
Stefano Nolfi
Stefano Nolfi National Research Council (CNR)
Angelo Cangelosi
Angelo Cangelosi University of Manchester
Giorgio Metta
Giorgio Metta Italian Institute of Technology
Kenji Doya
Kenji Doya Okinawa Institute of Science and Technology
Chrystopher L. Nehaniv
Chrystopher L. Nehaniv University of Waterloo

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