Integrating computational models, including Bayesian models, digital twins, and deep learning, for advancing pre-clinical and clinical neuroscience

Translational neuroscience is deeply engaged with the notion that the brain functions on computational principles, including Bayesian principles, striving to reduce environmental uncertainty through predictive mechanisms such as the free-energy principle. This foundational concept places predictive processing at the center of brain structure and function. Recent methodological advancements highlight the transformative potential of these approaches. However, despite these advances, the full capacity of the computational models remains largely untapped in specific applications within translational neuroscience. This Research Topic seeks to bridge that gap and broaden and explore the spectrum of computational models in translational neuroscience. Our goal is to gather original research papers, study protocols, and comprehensive reviews that illustrate the ways computational models can predict and simulate brain function under physiological and pathological conditions in human and animal studies. To further understand the extensive applicability of advanced models in neuroscience, we invite submissions addressing, but not limited to, the following themes:• Exploration of simulation and optimization models in translational neuroscience• Harnessing Bayesian compressive sensing techniques• Use of hierarchical models in neuroscience• Impact of model perspectives on magnitude estimation• Developing unbiased approaches to functional connectomics• Creating efficient spatial models for neuroimaging data• Leveraging Artificial Intelligence in translational neuroscienceThese contributions will provide an enriched perspective resource for effectively applying complex computational models to advance research in translational neuroscience. Topic editors: Reza Rastmanesh, Jacob Raber, Benjamin R Pittman-Polletta Source: https://www.frontiersin.org/research-topics/71215/integrating-computational-models-including-bayesian-models-digital-twins-and-deep-learning-for-advancing-pre-clinical-and-clinical-neuroscience