About this Research Topic
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Novel Applications of Bayesian and Other Models in Translational Neuroscience.
Translational neuroscience has long been intrigued by the theory that the brain operates on Bayesian principles, aiming to minimize environmental uncertainty through the free-energy principle. As a core concept, this suggests a foundational role of predictive processing in both brain function and structuring. Recent methodological advancements, such as variational Bayesian mixed-effects inference and multi-task Bayesian compressive sensing, underscore the potential of Bayesian approaches in neuroscience research. Despite these advancements, there remains a significant underutilization of Bayesian models in specialized applications within translational neuroscience, suggesting a gap in the exploration and application that could foster significant methodological innovations.
This Research Topic aims to illuminate and expand the spectrum of Bayesian and other computational models in translational neuroscience. By soliciting original papers, study protocols, and comprehensive reviews, we seek to uncover how these models can predict and anticipate brain function under both physiological and pathological states in human and animal models. The focus is to generate new technical and methodological insights that could revolutionize understandings in the field.
To gather further insights in the broad applicability of advanced models in neuroscience, we welcome articles addressing, but not limited to:
• Novel application of Bayesian optimizations and Bayesian simulation and other models in translational neuroscience
• Novel Applications of Bayesian inference and mixed-effects inference
• Potential of Bayesian compressive sensing approaches
• Hierarchical Bayesian and other models
• Perspective of Bayesian and other models on magnitude estimation
• Development of unbiased Bayesian and other approaches to functional connectomics
• Development of efficient Bayesian and other spatial models for neuroimaging data
• Application of Artificial Intelligence in Translational Neuroscience
These articles should contribute to a richer understanding of how complex Bayesian and alternative models can be effectively applied in translational neuroscience research.
Novel Applications of Bayesian and Other Models in Translational Neuroscience.
Translational neuroscience has long been intrigued by the theory that the brain operates on Bayesian principles, aiming to minimize environmental uncertainty through the free-energy principle. As a core concept, this suggests a foundational role of predictive processing in both brain function and structuring. Recent methodological advancements, such as variational Bayesian mixed-effects inference and multi-task Bayesian compressive sensing, underscore the potential of Bayesian approaches in neuroscience research. Despite these advancements, there remains a significant underutilization of Bayesian models in specialized applications within translational neuroscience, suggesting a gap in the exploration and application that could foster significant methodological innovations.
This Research Topic aims to illuminate and expand the spectrum of Bayesian and other computational models in translational neuroscience. By soliciting original papers, study protocols, and comprehensive reviews, we seek to uncover how these models can predict and anticipate brain function under both physiological and pathological states in human and animal models. The focus is to generate new technical and methodological insights that could revolutionize understandings in the field.
To gather further insights in the broad applicability of advanced models in neuroscience, we welcome articles addressing, but not limited to:
• Novel application of Bayesian optimizations and Bayesian simulation and other models in translational neuroscience
• Novel Applications of Bayesian inference and mixed-effects inference
• Potential of Bayesian compressive sensing approaches
• Hierarchical Bayesian and other models
• Perspective of Bayesian and other models on magnitude estimation
• Development of unbiased Bayesian and other approaches to functional connectomics
• Development of efficient Bayesian and other spatial models for neuroimaging data
• Application of Artificial Intelligence in Translational Neuroscience
These articles should contribute to a richer understanding of how complex Bayesian and alternative models can be effectively applied in translational neuroscience research.
Keywords: Translational Neuroscience, Bayesian and other models, Data Analysis, Probabilistic and Bayesian Analytics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
