Kenji Doya

发表

Learning algorithms for modeling human behaviors and identifying molecular cascades

Kenji Doya, K. Doya, 1970 .

Learning Sensory Feedback to CPG with Policy Gradient for Biped Locomotion

Jun Morimoto, Jun Nakanishi, Takamitsu Matsubara, 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data

Junichiro Yoshimoto, Kenji Doya, Kosuke Yoshida, 2017, BMC Bioinformatics.

Learning CPG-based biped locomotion with a policy gradient method

Jun Morimoto, Jun Nakanishi, Takamitsu Matsubara, 2005, Humanoids.

Neural Networks letter Meta-learning in Reinforcement Learning

Kenji Doya, Nicolas Schweighofer, K. Doya, 2003 .

A Novel Reinforcement Model of Birdsong Vocalization Learning

Terrence J. Sejnowski, Kenji Doya, T. Sejnowski, 1994, NIPS.

Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming pdf

Kenji Doya, Eiji Uchibe, Tadashi Kozuno, 2017, ArXiv.

Model-based Empowerment Computation for Dynamical Agents

Kenji Doya, Takashi Ikegami, Yuma Kajihara, 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

Self-consistent neuronal population under spike inputs and unbalanced conditions

Kenji Doya, Carlos E. Gutierrez, Junichiro Yoshimoto, 2015, 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).

Online meta-learning by parallel algorithm competition

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2018, GECCO.

Natural actor-critic with baseline adjustment for variance reduction

Kenji Doya, Tetsuro Morimura, Eiji Uchibe, 2008, Artificial Life and Robotics.

Neural Networks goes electronic at twenty!

Stephen Grossberg, John G. Taylor, Kenji Doya, 2008, Neural Networks.

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions

Junichiro Yoshimoto, Kenji Doya, Ildefons Magrans de Abril, 2017, Neural Networks.

Responding to Modalities with Different Latencies

Kenji Doya, Hiroyuki Nakahara, Okihide Hikosaka, 2004, NIPS.

Policy gradient reinforcement learning method for discrete-time linear quadratic regulation problem using estimated state value function

Hirokazu Anai, Kenji Doya, Hidenao Iwane, 2017, 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).

Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning pdf

Dongqi Han, Kenji Doya, Tadashi Kozuno, 2019, ArXiv.

Adaptation of optimization algorithms to problem domains by transfer learning

Kenji Doya, Chris Reinke, 2017, 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).

Analysis of Structure-Function Relationship Using a Whole-Brain Dynamic Model Based on MRI Images of the Common Marmoset

Shin Ishii, Ken Nakae, Kenji Doya, 2018 .

Robust Population Coding in Free-Energy-Based Reinforcement Learning

Junichiro Yoshimoto, Kenji Doya, Makoto Otsuka, 2008, ICANN.

Emergence of Different Mating Strategies in Artificial Embodied Evolution

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2009, ICONIP.

Adaptation in Dynamic Environments by Learning and Evolution

Kenji Doya, Genci Capi, Masao Yokota, 2004 .

Average Reward Optimization with Multiple Discounting Reinforcement Learners

Kenji Doya, Eiji Uchibe, Chris Reinke, 2017, ICONIP.

Scaled free-energy based reinforcement learning for robust and efficient learning in high-dimensional state spaces

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2013, Front. Neurorobot..

APPLICATION OF EVOLUTIONARY COMPUTATION FOR EFFICIENT REINFORCEMENT LEARNING

Kenji Doya, Genci Capi, K. Doya, 2006, Appl. Artif. Intell..

Driver model based on reinforced learning with multiple‐step state estimation

Kenji Doya, Yasuharu Koike, K. Doya, 2003 .

Announcement of the Neural Networks Best Paper Award

DeLiang Wang, Kenji Doya, K. Doya, 2017, Neural Networks.

Metalearning and neuromodulation

Kenji Doya, K. Doya, 2002, Neural Networks.

Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference

Shin Ishii, Ken Nakae, Kenji Doya, 2019, Scientific reports.

Temporal Difference Learning in Continuous Time and Space

Kenji Doya, K. Doya, 1995, NIPS.

State of Neural Networks Is Strong

DeLiang Wang, Kenji Doya, K. Doya, 2016, Neural Networks.

Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning

Kenji Doya, Eiji Uchibe, Tadashi Kozuno, 2019, AISTATS.

Bifurcations of Recurrent Neural Networks in Gradient Descent Learning

Kenji Doya, K. Doya, 1993 .

Neural Information Processing

Akira Hirose, Minho Lee, Derong Liu, 2016, Lecture Notes in Computer Science.

An excellent year and a transition

DeLiang Wang, John G. Taylor, Kenji Doya, 2011, Neural Networks.

Meta-learning in Reinforcement Learning

Kenji Doya, Nicolas Schweighofer, K. Doya, 2003, Neural Networks.

Biologically inspired embodied evolution of survival

Henrik I. Christensen, Kenji Doya, Stefan Elfwing, 2005, 2005 IEEE Congress on Evolutionary Computation.

Multiple Model-Based Reinforcement Learning

Mitsuo Kawato, Kenji Doya, Kazuyuki Samejima, 2002, Neural Computation.

Learning a dynamic policy by using policy gradient: application to biped walking

Jun Morimoto, Jun Nakanishi, Takamitsu Matsubara, 2007, Syst. Comput. Jpn..

Neural Information Processing

Akira Hirose, Kazushi Ikeda, Kenji Doya, 2014, Lecture Notes in Computer Science.

Hierarchical Chunking during Learning of Visuomotor Sequences

Ahmed, Kenji Doya, Raju S. Bapi, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Mini-special issue: ICONIP 2007

Masumi Ishikawa, Kenji Doya, K. Doya, 2008, Neural Networks.

Neural Information Processing

Akira Hirose, Minho Lee, Derong Liu, 2016, Lecture Notes in Computer Science.

Free-energy-based reinforcement learning in a partially observable environment

Junichiro Yoshimoto, Kenji Doya, Makoto Otsuka, 2010, ESANN.

S3f2-5 Learning model-based analysis of neuroimaging data(S3-f2: "Advances in Anatomical, Functional, and Computational Brain Imaging",Symposia,Abstract,Meeting Program of EABS & BSJ 2006)

Kenji Doya, K. Doya, 2006 .

Neural circuits for reinforcement learning and mental simulation

Kenji Doya, K. Doya, 2019, IBRO Reports.

A Kinetic Model of Dopamine- and Calcium-Dependent Striatal Synaptic Plasticity

Junichiro Yoshimoto, Kenji Doya, Takashi Nakano, 2010, PLoS Comput. Biol..

Estimating Internal Variables and Paramters of a Learning Agent by a Particle Filter

Kenji Doya, Minoru Kimura, Kazuyuki Samejima, 2003, NIPS.

Multiple state estimation reinforcement learning for driving model: driver model of automobile

Yasuharu Koike, Kenji Doya, K. Doya, 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning

Jun Morimoto, Kenji Doya, K. Doya, 2000, Robotics Auton. Syst..

Computational Complexity Reduction for Functional Connectivity Estimation in Large Scale Neural Network

Junichiro Yoshimoto, Shin Ishii, Kenji Doya, 2015, ICONIP.

A Model of the Electrophysiological Properties of the Inferior Olive Neurons

Mitsuo Kawato, Kenji Doya, Nicolas Schweighofer, 1998, ICONIP.

Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2017, Neural Networks.

Symbolization and Imitation Learning of Motion Sequence Using Competitive Modules

Kenji Doya, Kazuyuki Samejima, Ken'Ichi Katagiri, 2006 .

Neural Information Processing

Akira Hirose, Minho Lee, Derong Liu, 2016, Lecture Notes in Computer Science.

Molecular and network mechanisms of reinforcement learning

Kenji Doya, K. Doya, 2004 .

Adaptive neural oscillator using continuous-time back-propagation learning

Kenji Doya, Shuji Yoshizawa, K. Doya, 1989, Neural Networks.

Unbounded Output Networks for Classification pdf

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2018, ArXiv.

Reinforcement learning with multiple representations in the basal ganglia loops for sequential motor control

Kenji Doya, Hiroyuki Nakahara, Okihide Hikosaka, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Maximizing the average reward in episodic reinforcement learning tasks

Kenji Doya, Eiji Uchibe, Chris Reinke, 2015, 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).

Multi-agent reinforcement learning: an approach based on the other agent's internal model

Shin Ishii, Kenji Doya, Yasuo Nagayuki, 2000, Proceedings Fourth International Conference on MultiAgent Systems.

Reinforcement Learning and Computational Neuroscience

Kenji Doya, K. Doya, 2002 .

Designing the Reward System: Computational and Biological Principles

Kenji Doya, 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

Announcement of the Neural Networks Best Paper Award

DeLiang Wang, Kenji Doya, K. Doya, 2017, Neural Networks.

Switching particle filters for efficient real-time visual tracking

Shin Ishii, Kenji Doya, Takashi Bando, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Model-based analysis of decision variables

Makoto Ito, Kenji Doya, Kazuyuki Samejima, 2011 .

Expected energy-based restricted Boltzmann machine for classification

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2015, Neural Networks.

Neural network model of temporal pattern memory

Kenji Doya, Shuji Yoshizawa, K. Doya, 1991, Systems and Computers in Japan.

The Mechanism of Saccade Motor Pattern Generation Investigated by a Large-Scale Spiking Neuron Model of the Superior Colliculus

Kenji Doya, Tomohiro Shibata, Jan Morén, 2013, PloS one.

Inverse reinforcement learning using Dynamic Policy Programming

Kenji Doya, Eiji Uchibe, K. Doya, 2014, 4th International Conference on Development and Learning and on Epigenetic Robotics.

Toward Probabilistic Diagnosis and Understanding of Depression Based on Functional MRI Data Analysis with Logistic Group LASSO

Kenji Doya, Junichiro Yoshimoto, Yasumasa Okamoto, 2015, PloS one.

Multiple model-based reinforcement learning explains dopamine neuronal activity

Kenji Doya, Nicolas Schweighofer, Mathieu Bertin, 2007, Neural Networks.

Creating Brain-Like Intelligence: From Basic Principles to Complex Intelligent Systems

Bernhard Sendhoff, Olaf Sporns, Kenji Doya, 2009, Creating Brain-Like Intelligence.

A computational neural model of goal-directed utterance selection

Günther Palm, Kenji Doya, Hans Kamp, 2010, Neural Networks.

Promoting Further Developments of Neural Networks

DeLiang Wang, Kenji Doya, K. Doya, 2017, Neural Networks.

Different Cortico-Basal Ganglia Loops Specialize in Reward Prediction at Different Time Scales

Kenji Doya, Kazutaka Ueda, Yasumasa Okamoto, 2003, NIPS.

Adaptive Synchronization of Neural and Physical Oscillators

Kenji Doya, Shuji Yoshizawa, K. Doya, 1991, NIPS.

Learning CPG-based biped locomotion with a policy gradient method

Jun Morimoto, Jun Nakanishi, Takamitsu Matsubara, 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?

Kenji Doya, K. Doya, 1999, Neural Networks.

Hierarchical Bayesian estimation for MEG inverse problem

Masa-aki Sato, Mitsuo Kawato, Kenji Doya, 2004, NeuroImage.

Nitric Oxide Regulates Input Specificity of Long-Term Depression and Context Dependence of Cerebellar Learning

Mitsuo Kawato, Kenji Doya, Hideaki Ogasawara, 2006, PLoS Comput. Biol..

MOSAIC Reinforcement Learning Architecture: Symbolization by Predictability and Mimic Learning by Symbol

Mitsuo Kawato, Kenji Doya, Kazuyuki Samejima, 2001 .

New action editors join the journal! Five exciting special issues in the works!

Stephen Grossberg, John G. Taylor, Kenji Doya, 2009, Neural Networks.

Evolution of meta-parameters in reinforcement learning algorithm

Kenji Doya, Genci Capi, Anders Eriksson, 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

Switching particle filters for efficient visual tracking

Shin Ishii, Kenji Doya, Takashi Bando, 2006, Robotics Auton. Syst..

Emergence of Hierarchy via Reinforcement Learning Using a Multiple Timescale Stochastic RNN pdf

Dongqi Han, Kenji Doya, Jun Tani, 2019, ArXiv.

Universality of Fully-Connected Recurrent Neural Networks

Kenji Doya, K. Doya, 1993 .

Changing the structure of complex visuo-motor sequences selectively activates the fronto-parietal network

Ahmed, Kenji Doya, Raju S. Bapi, 2012, NeuroImage.

Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning

Mitsuo Kawato, Kenji Yamamoto, Kenji Doya, 2001, Biological Cybernetics.

Inter-module credit assignment in modular reinforcement learning

Mitsuo Kawato, Kenji Doya, Kazuyuki Samejima, 2003, Neural Networks.

Maaping Between Neural and Physical Activities of the Lobster Gastric Mill

Kenji Doya, Allen I. Selverston, Mary E. T. Boyle, 1992, NIPS.

Designing theRewardSystem: Computational andBiological Principles

Kenji Doya, 2007 .

Memorizing hierarchical temporal patterns in analog neuron networks

Kenji Doya, Shuji Yoshizawa, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

Reinforcement Learning State Estimator

Jun Morimoto, Kenji Doya, K. Doya, 2007, Neural Computation.

Hierarchical reinforcement learning for motion learning: learning 'stand-up' trajectories

Jun Morimoto, Kenji Doya, K. Doya, 1998, Adv. Robotics.

Free-Energy Based Reinforcement Learning for Vision-Based Navigation with High-Dimensional Sensory Inputs

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2010, ICONIP.

A Generalized Natural Actor-Critic Algorithm

Junichiro Yoshimoto, Kenji Doya, Tetsuro Morimura, 2009, NIPS.

Benefit of Multiple Representations for Motor Sequence Control in The Basal Ganglia Loops

Kenji Doya, Hiroyuki Nakahara, Okihide Hikosaka, 1998 .

A hierarchical Bayesian method to resolve an inverse problem of MEG contaminated with eye movement artifacts

Masa-aki Sato, Mitsuo Kawato, Kenji Doya, 2009, NeuroImage.

Calcium Responses Model in Striatum Dependent on Timed Input Sources

Junichiro Yoshimoto, Kenji Doya, Jeffery R. Wickens, 2009, ICANN.

Darwinian embodied evolution of the learning ability for survival

Henrik I. Christensen, Kenji Doya, Stefan Elfwing, 2011, Adapt. Behav..

Finding Exploratory Rewards by Embodied Evolution and Constrained Reinforcement Learning in the Cyber Rodents

Kenji Doya, Eiji Uchibe, K. Doya, 2007, ICONIP.

Evaluation of linearly solvable Markov decision process with dynamic model learning in a mobile robot navigation task

Kenji Doya, Eiji Uchibe, Ken Kinjo, 2013, Front. Neurorobot..

Emergence of communication among reinforcement learning agents under coordination environment

Qiong Huang, Kenji Doya, Eiji Uchibe, 2016, 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob).

Loss of a Co-Editor-in-Chief and friend

DeLiang Wang, Kenji Doya, K. Doya, 2012, Neural Networks.

Variational Recurrent Models for Solving Partially Observable Control Tasks pdf

Dongqi Han, Kenji Doya, Jun Tani, 2019, ICLR.

Conference on Intelligent Robots and Systems Reinforcement Le,arning of Dynamic Motor Sequence: Learning to Stand Up

Jun Morimoto, Kenji Doya, K. Doya, 2022 .

Reinforcement learning with via-point representation

Jun Morimoto, Mitsuo Kawato, Kenji Doya, 2004, Neural Networks.

Chunking Phenomenon in Complex Sequential Skill Learning in Humans

Kenji Doya, Raju S. Bapi, Krishna P. Miyapuram, 2004, ICONIP.

Neural Computations Supporting Cognition: Rumelhart Prize Symposium in Honor of Peter Dayan

Alexandre Pouget, Kenji Doya, Nathaniel D. Daw, 2012, CogSci.

Bayesian System Identification of Molecular Cascades

Junichiro Yoshimoto, Kenji Doya, K. Doya, 2007, ICONIP.

A Spiking Neural Network Model of Model-Free Reinforcement Learning with High-Dimensional Sensory Input and Perceptual Ambiguity

Kenji Doya, Takashi Nakano, Junichiro Yoshimoto, 2015, PloS one.

Reinforcement Learning using Importance Sampling for Multiple Learning Modules

Kenji Doya, Eiji Uchibe, K. Doya, 2003 .

A New Natural Policy Gradient by Stationary Distribution Metric

Junichiro Yoshimoto, Kenji Doya, Tetsuro Morimura, 2008, ECML/PKDD.

Fostering deep learning and beyond

DeLiang Wang, Kenji Doya, K. Doya, 2018, Neural Networks.

Robust Reinforcement Learning

Jun Morimoto, Kenji Doya, K. Doya, 2005, Neural Computation.

A model-based prediction of the calcium responses in the striatal synaptic spines depending on the timing of cortical and dopaminergic inputs and post-synaptic spikes

Junichiro Yoshimoto, Kenji Doya, Takashi Nakano, 2013, Front. Comput. Neurosci..

Multi-agent reinforcement learning: using macro actions to learn a mating task

Henrik I. Christensen, Kenji Doya, Stefan Elfwing, 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

NeuroEvolution Based on Reusable and Hierarchical Modular Representation

Kenji Doya, Takumi Kamioka, Eiji Uchibe, 2008, ICONIP.

Evolution of Neural Architecture Fitting Environmental Dynamics

Kenji Doya, Genci Capi, K. Doya, 2005, Adapt. Behav..

Learning of Hierarchical Temporal Sequences in Biological Systems.

Kenji Doya, Chikara Hoshino, K. Doya, 2000 .

Learning how, what, and whether to communicate: emergence of protocommunication in reinforcement learning agents

Kenji Doya, Takashi Sato, Eiji Uchibe, 2007, Artificial Life and Robotics.

Combining Modalities with Different Latencies for Optimal Motor Control

Kenji Doya, Hiroyuki Nakahara, Okihide Hikosaka, 2008, Journal of Cognitive Neuroscience.

Evolutionary Development of Hierarchical Learning Structures

Henrik I. Christensen, Kenji Doya, Stefan Elfwing, 2007, IEEE Transactions on Evolutionary Computation.

Behavioral Data Analysis by Reinforcement Learning Models

Kenji Doya, Kazuyuki Samejima, K. Doya, 2007 .

PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos pdf

Carl E. Rasmussen, Jan Peters, Kenji Doya, 2019, ICML.

Robustness of linearly solvable Markov games employing inaccurate dynamics model

Kenji Doya, Eiji Uchibe, Ken Kinjo, 2018, Artificial Life and Robotics.

Toward a Spiking-Neuron Model of the Oculomotor System

Kenji Doya, Tomohiro Shibata, Jan Morén, 2010, SAB.

Reinforcement learning: Computational theory and biological mechanisms

Kenji Doya, K. Doya, 2007, HFSP journal.

Humans Can Adopt Optimal Discounting Strategy under Real-Time Constraints

Kenji Doya, Nicolas Schweighofer, Yasumasa Okamoto, 2006, PLoS Comput. Biol..

A Sequence Learning Architecture Based on Cortico-Basal Ganglionic Loops and Reinforcement Learning

Kenji Doya, Raju S. Bapi, 1998, ICONIP.

Erratum to "Brain mechanism of reward prediction under predictable and unpredictable environmental dynamics" [Neural Networks 19 (8) (2006) 1233-1241]

Kenji Doya, Kazutaka Ueda, Yasumasa Okamoto, 2007, Neural Networks.

Evolution of recurrent neural controllers using an extended parallel genetic algorithm

Kenji Doya, Genci Capi, K. Doya, 2005, Robotics Auton. Syst..

Imitation Learning by Estimating Intention of Demonstrator

Mitsuo Kawato, Norikazu Sugimoto, Kenji Doya, 2003 .

Evolving recurrent neural controllers for sequential tasks: a parallel implementation

Kenji Doya, Genci Capi, K. Doya, 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

Reinforcement Learning in Continuous Time and Space

Kenji Doya, K. Doya, 2000, Neural Computation.

Finding intrinsic rewards by embodied evolution and constrained reinforcement learning

Kenji Doya, Eiji Uchibe, K. Doya, 2008, Neural Networks.

Reinforcement learning with state-dependent discount factor

Naoto Yoshida, Kenji Doya, Eiji Uchibe, 2013, 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL).

Near-Saddle-Node Bifurcation Behavior as Dynamics in Working Memory for Goal-Directed Behavior

Kenji Doya, Hiroyuki Nakahara, K. Doya, 1998, Neural Computation.

Dimension Reduction of Biological Neuron Models by Artificial Neural Networks

Kenji Doya, Allen I. Selverston, K. Doya, 1994, Neural Computation.

Efficient Nonlinear Control with Actor-Tutor Architecture

Kenji Doya, K. Doya, 1996, NIPS.

[Artificial Intelligence and Brain Science: the Present and the Future].

Kenji Doya, Yutaka Matsuo, 2019, Brain and nerve = Shinkei kenkyu no shinpo.

From free energy to expected energy: Improving energy-based value function approximation in reinforcement learning

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2016, Neural Networks.

Learning and Control

Hidenori Kimura, Mitsuo Kawato, Kenji Doya, 2001 .

Introduction for 2002 Special Issue: Computational Models of Neuromodulation

Kenji Doya, Michael E. Hasselmo, Peter Dayan, 2002, Neural Networks.

Multiple model-based reinforcement learning for nonlinear control

Mitsuo Kawato, Kenji Doya, Kazuyuki Samejima, 2006 .

Editorial for 2010

John G. Taylor, Kenji Doya, Stephen Grossberg, 2010, Neural Networks.

MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography pdf

Shin Ishii, Ken Nakae, Kenji Doya, 2019, ArXiv.

Expedited review process

DeLiang Wang, John G. Taylor, Kenji Doya, 2012, Neural Networks.

Adaptive Baseline Enhances EM-Based Policy Search: Validation in a View-Based Positioning Task of a Smartphone Balancer

Kenji Doya, Jiexin Wang, Eiji Uchibe, 2017, Front. Neurorobot..

Neurocomputational models of brain disorders

Wlodzislaw Duch, Kenji Doya, Vassilis Cutsuridis, 2011, Neural Networks.

An Experimental Study of Emergence of Communication of Reinforcement Learning Agents

Qiong Huang, Kenji Doya, 2019, AGI.

MOSAIC for Multiple-Reward Environments

Mitsuo Kawato, Norikazu Sugimoto, Kenji Doya, 2012, Neural Computation.

An Evolutionary Approach to Automatic Construction of the Structure in Hierarchical Reinforcement Learning

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2003, GECCO.

A Study of Learning Algorithms for Continuous-TimeRecurrent Neural Networks

Kenji Doya, K. Doya, 1991 .

Parallel Representation of Value-Based and Finite State-Based Strategies in the Ventral and Dorsal Striatum

Makoto Ito, Kenji Doya, K. Doya, 2015, PLoS Comput. Biol..

Resting state functional connectivity explains individual scores of multiple clinical measures for major depression

Junichiro Yoshimoto, Kenji Doya, Kosuke Yoshida, 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillations

Kenji Doya, Allen I. Selverston, Peter F. Rowat, 1993, NIPS.

Reinforcement learning under constraints generated by multiple reward functions

Kenji Doya, Eiji Uchibe, K. Doya, 2006 .

Dynamics of Attention as Near Saddle-Node Bifurcation Behavior

Kenji Doya, Hiroyuki Nakahara, 1995, NIPS.

Acquisition of Stand-up Behavior by a 3-link 2-joint Robot using Hierarchical Reinforcement Learning

Jun Morimoto, Kenji Doya, K. Doya, 2001 .

Co-evolution of Shaping Rewards and Meta-Parameters in Reinforcement Learning

Henrik I. Christensen, Kenji Doya, Stefan Elfwing, 2008, Adapt. Behav..

How can we learn efficiently to act optimally and flexibly?

Kenji Doya, K. Doya, 2009, Proceedings of the National Academy of Sciences.

Inter Subject Correlation of Brain Activity during Visuo-Motor Sequence Learning

Kenji Doya, Raju S. Bapi, Krishna P. Miyapuram, 2014, ICONIP.

fMRI investigation of cortical and subcortical networks in the learning of abstract and effector-specific representations of motor sequences

Kenji Doya, Raju S. Bapi, Krishna P. Miyapuram, 2006, NeuroImage.

Neural Information Processing, 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I

Masumi Ishikawa, Takeshi Yamakawa, Kenji Doya, 2008, ICONIP.

Imitation learning based on entropy-regularized forward and inverse reinforcement learning pdf

Kenji Doya, Eiji Uchibe, 2020, ArXiv.

Hierarchical Reinforcement Learning of Low-Dimensional Subgoals and High-Dimensional Trajectories

Jun Morimoto, Kenji Doya, K. Doya, 1998, ICONIP.

The Cyber Rodent Project: Exploration of Adaptive Mechanisms for Self-Preservation and Self-Reproduction

Kenji Doya, Eiji Uchibe, K. Doya, 2005, Adapt. Behav..

Brain mechanism of reward prediction under predictable and unpredictable environmental dynamics

Kenji Doya, Kazutaka Ueda, Yasumasa Okamoto, 2006, Neural Networks.

Derivatives of Logarithmic Stationary Distributions for Policy Gradient Reinforcement Learning

Junichiro Yoshimoto, Jan Peters, Kenji Doya, 2010, Neural Computation.

Co-evolution of Rewards and Meta-parameters in Embodied Evolution

Kenji Doya, Stefan Elfwing, Eiji Uchibe, 2009, Creating Brain-Like Intelligence.

Combining learned controllers to achieve new goals based on linearly solvable MDPs

Kenji Doya, Eiji Uchibe, 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

Multi-scale, multi-modal neural modeling and simulation

Shin Ishii, Markus Diesmann, Kenji Doya, 2011, Neural Networks.

Efficient schema matching algorithm based on pre-checking

Jun Morimoto, Jun Nakanishi, Takamitsu Matsubara, 2007 .

Adaptive Selection of Learning Strategy for Autonomous Sequence Learning in Rats

Kenji Doya, Hiroshi Tsujino, Masato Hoshino, 2010, Aust. J. Intell. Inf. Process. Syst..

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

Kenji Doya, Junichiro Yoshimoto, Yasumasa Okamoto, 2015, PloS one.

Recurrent networks: supervised learning

Kenji Doya, K. Doya, 1998 .

EM-based policy hyper parameter exploration: application to standing and balancing of a two-wheeled smartphone robot

Kenji Doya, Jiexin Wang, Eiji Uchibe, 2015, Artificial Life and Robotics.

Multiple Forward Model Architecture for Sequence Processing

Kenji Doya, Raju S. Bapi, 2001, Sequence Learning.

Estimating Internal Variables of a Decision Maker's Brain: A Model-Based Approach for Neuroscience

Kenji Doya, Kazuyuki Samejima, K. Doya, 2007, ICONIP.

Effects of transcranial direct current stimulation in brain-computer interface

Takashi Hanakawa, Kenji Doya, Yousuke Ogata, 2021, 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech).

Goal-Directed Planning by Reinforcement Learning and Active Inference pdf

Dongqi Han, Kenji Doya, Jun Tani, 2021, ArXiv.

Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots pdf

Takayuki Nagai, Hiroshi Yamakawa, Kenji Doya, 2021, Neural Networks.

Forward and inverse reinforcement learning sharing network weights and hyperparameters

Kenji Doya, Eiji Uchibe, 2021, Neural Networks.

Announcement of the Neural Networks Best Paper Award

Kenji Doya, DeLiang Wang, K. Doya, 2022, Neural Networks.

Multiple Representations and Algorithms for Sequence Learning

Kenji Doya, Raju S. Bapi, Hiroyuki Nakahara, 1999 .

Computational Model of Recurrent Subthalamo-Pallidal Circuit for Generation of Parkinsonian Oscillations

Kenji Doya, Atsushi Nambu, Osamu Shouno, 2017, Front. Neuroanat..

Neuroethics Questions to Guide Ethical Research in the International Brain Initiatives

Anna Devor, Arisa Ema, Kenji Doya, 2018, Neuron.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

Kenji Doya, Kosuke Yoshida, Junichiro Yoshimoto, 2017, PloS one.

Computation of Driving Pleasure based on Driver's Learning Process Simulation by Reinforcement Learning

Kenji Doya, Eiji Uchibe, Yonosuke Miki, 2013 .

Emergence of Polymorphic Mating Strategies in Robot Colonies

Kenji Doya, Stefan Elfwing, K. Doya, 2014, PloS one.

MFM: Multiple Forward Model Architecture for Sequence Processing

Kenji Doya, Raju S. Bapi, 1999 .

Three-dimensional distribution of Fos-positive neurons in the supramammillary nucleus of the rat exposed to novel environment

Makoto Ito, Kenji Doya, Tomoaki Shirao, 2009, Neuroscience Research.

Recurrent Networks : Learning Algorithms ∗

Kenji Doya, K. Doya, 2002 .

Identification of depression subtypes and relevant brain regions using a data-driven approach

Kenji Doya, Junichiro Yoshimoto, Yasumasa Okamoto, 2018, Scientific Reports.

Self-Reproduction The Cyber Rodent Project : Exploration of Adaptive Mechanisms for Self-Preservation and

Kenji Doya, Eiji Uchibe, K. Doya, 2005 .

Reward probability and timing uncertainty alter the effect of dorsal raphe serotonin neurons on patience

Kenji Doya, Katsuhiko Miyazaki, Akihiro Yamanaka, 2018, Nature Communications.

Increased serotonin efflux in the dorsal raphe nucleus of rats working for delayed rewards

Kenji Doya, Kayoko Miyazaki, Katsuhiko Miyazaki, 2007, Neuroscience Research.