What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Neuroscience
Stefan J. Kiebel mainly focuses on Artificial intelligence, Neuroscience, Bayesian inference, Bayesian probability and Inference.
His studies in Artificial intelligence integrate themes in fields like Dynamical systems theory, Machine learning, Dynamic causal modelling and Causal model.
His Causal model research integrates issues from Magnetoencephalography, Electroencephalography, System dynamics, Mathematical model and Functional neuroimaging.
His research is interdisciplinary, bridging the disciplines of Audiology and Neuroscience.
As a part of the same scientific family, Stefan J. Kiebel mostly works in the field of Bayesian probability, focusing on Pattern recognition and, on occasion, Prior probability and Preprocessor.
His Inference research incorporates elements of Categorization, Perception and Hierarchy.
His most cited work include:
- Statistical Parametric Mapping: The Analysis of Functional Brain Images (2078 citations)
- Predictive coding under the free-energy principle. (703 citations)
- Classical and Bayesian inference in neuroimaging: applications. (609 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Neuroscience, Bayesian inference, Bayesian probability and Inference.
Stefan J. Kiebel has researched Artificial intelligence in several fields, including Perception, Dynamic causal modelling, Machine learning, Causal model and Pattern recognition.
Electroencephalography, Electrophysiology, Brain mapping, Visual cortex and Sensory system are the primary areas of interest in his Neuroscience study.
He works on Electroencephalography which deals in particular with Magnetoencephalography.
His work in the fields of Bayesian inference, such as Bayesian statistics, overlaps with other areas such as Generative model.
His Bayesian probability research focuses on Bayes' theorem and Prior probability.
He most often published in these fields:
- Artificial intelligence (41.50%)
- Neuroscience (29.00%)
- Bayesian inference (23.00%)
What were the highlights of his more recent work (between 2018-2021)?
- Neuroscience (29.00%)
- Cognition (5.50%)
- Cognitive psychology (13.50%)
In recent papers he was focusing on the following fields of study:
Stefan J. Kiebel mainly investigates Neuroscience, Cognition, Cognitive psychology, Inference and Sensory system.
His Neuroscience research includes elements of Value and Adaptation.
His research in Cognition intersects with topics in Dilemma, Perception and Set.
His work deals with themes such as Factor graph, Belief propagation, Bayesian probability, Biological network and Variational message passing, which intersect with Inference.
His Bayesian probability study improves the overall literature in Artificial intelligence.
His study in Sensory system is interdisciplinary in nature, drawing from both Tonotopy, Auditory cortex, Speech recognition and Thalamus.
Between 2018 and 2021, his most popular works were:
- Neuronal message passing using Mean-field, Bethe, and Marginal approximations. (52 citations)
- Addiction Research Consortium: Losing and regaining control over drug intake (ReCoDe)-From trajectories to mechanisms and interventions. (40 citations)
- Somatostatin Interneurons Promote Neuronal Synchrony in the Neonatal Hippocampus. (16 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Neuroscience
His primary areas of investigation include Inference, Neuroscience, Cognition, Hidden Markov model and Cognitive psychology.
His Inference study is focused on Artificial intelligence in general.
His Neuroscience study frequently involves adjacent topics like Network dynamics.
His Cognition study integrates concerns from other disciplines, such as Stressor and Set.
The Hidden Markov model study combines topics in areas such as Theoretical computer science, Approximate inference, Behavioral modeling, Representation and Reinforcement learning.
His biological study spans a wide range of topics, including Statistical model and Dilemma.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
