What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Machine learning
Kenji Doya spends much of his time researching Neuroscience, Reinforcement learning, Artificial intelligence, Basal ganglia and Cerebellum.
His study in the field of Striatum and Neuron is also linked to topics like Serotonergic and Dorsal raphe nucleus.
Kenji Doya combines subjects such as Nonlinear control, Robot, Neurochemical and Consumer neuroscience with his study of Reinforcement learning.
His research in Artificial intelligence intersects with topics in Machine learning and Control theory.
His Basal ganglia research incorporates themes from Neural correlates of consciousness and Cortex.
His Cerebellum research integrates issues from Cerebral cortex, Unsupervised learning and Hebbian theory.
His most cited work include:
- A unifying computational framework for motor control and social interaction (906 citations)
- Representation of action-specific reward values in the striatum. (723 citations)
- Reinforcement Learning in Continuous Time and Space (719 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Reinforcement learning, Neuroscience, Machine learning and Basal ganglia.
His research on Artificial intelligence frequently connects to adjacent areas such as Pattern recognition.
The various areas that Kenji Doya examines in his Reinforcement learning study include Robot, Unsupervised learning and Mathematical optimization.
Robot is closely attributed to Control theory in his work.
His work on Striatum and Cerebellum as part of general Neuroscience study is frequently linked to Serotonergic, Action selection and Dorsal raphe nucleus, bridging the gap between disciplines.
Kenji Doya regularly ties together related areas like Cerebral cortex in his Basal ganglia studies.
He most often published in these fields:
- Artificial intelligence (46.77%)
- Reinforcement learning (40.32%)
- Neuroscience (28.39%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (46.77%)
- Reinforcement learning (40.32%)
- Neuroscience (28.39%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Reinforcement learning, Neuroscience, Basal ganglia and Artificial neural network.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, State and Pattern recognition.
In general Reinforcement learning, his work in Q-learning is often linked to Simple linking many areas of study.
His study in the fields of Functional connectivity, Cerebral cortex and Cognition under the domain of Neuroscience overlaps with other disciplines such as Dorsal raphe nucleus and Serotonergic.
His work on Indirect pathway of movement is typically connected to Action selection as part of general Basal ganglia study, connecting several disciplines of science.
The study incorporates disciplines such as Markov decision process and Robustness in addition to Mathematical optimization.
Between 2015 and 2021, his most popular works were:
- Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex. (165 citations)
- Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. (160 citations)
- Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression (47 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Neuroscience
His primary areas of study are Artificial intelligence, Reinforcement learning, Neuroscience, Softmax function and Mathematical optimization.
His Artificial intelligence study incorporates themes from Machine learning, Functional magnetic resonance imaging and Pattern recognition.
Kenji Doya interconnects Artificial neural network, Function approximation, Deep learning and Reinforcement in the investigation of issues within Reinforcement learning.
His work on Neuroscience is being expanded to include thematically relevant topics such as Bayes' theorem.
His Mathematical optimization course of study focuses on Robustness and Markov decision process, Bellman equation and Restricted Boltzmann machine.
In his study, which falls under the umbrella issue of Basal ganglia, Operant conditioning and Ventromedial prefrontal cortex is strongly linked to Cerebellum.
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