Over the years, it has become clearer and clearer how the brain is a multiscale system that needs to be studied at different levels of organization as brain activity occurs across different spatial and temporal scales. Models of neural dynamics targeting these diverse scales (from the microlevel of neurons, to the mesoscale of neuronal populations, to the macroscale of large-scale brain networks) and facilitating their integration not only advance our understanding of brain function and dysfunction but also provide a basis for application in artificial systems.

Practically, such models could be used to develop brain-inspired algorithms (e.g., architectures for body control) and to build brain-computer interfaces (e.g., based on recordings of neurophysiological data) and may be translated to neuromorphic controllers (e.g., hardware interfaces with external effectors) to evaluate robot performance in different tasks to build artificial systems better suited for deployment in society. They could also be employed in the clinical practice for personalized medicine, for example, as aids for the diagnosis of neurological disorders or by building brain digital twins able to reliably predict the effects of potential treatments or drugs.

In the field of neurorobotics, where biologically inspired neural models guide the development and control of robots, multiscale brain modeling has significant potential. These models not only provide insights into brain dynamics at different scales, allowing more realistic and adaptive robotic behavior, but also permit scientists to derive hypotheses that can be tested using artificial systems. Some possible application of multiscale brain modeling in this field are the design of neuromorphic control systems (e.g., for movement control and sensorimotor integration), the transfer of human-like learning and decision-making into robots allowing them to learn and adapt to new environments and tasks more effectively, and the development of brain-machine interfaces allowing humans to control robots or prosthetic devices with their brain activity.

This Research Topic will highlight novel advancements in multiscale modeling of brain dynamics, fostering collaboration between neuroscience, psychology, robotics, AI, and clinical research. Contributions spanning the development of novel models, benchmarking and validation through experimental data, and their application in both the artificial systems and the clinical practice are particularly valuable. We welcome all article types, including research reports, reviews, methods papers, perspectives, and hypothesis and theory contributions.

This article collection aims to catch the growing interest of the research community around this topic and to capitalize on the enthusiasm elicited by the development and testing of the first promising models of multiscale brain dynamics.

Potential topics include, but are not limited to, the following:

• Multiscale modeling techniques
• Network architectures
• Modeling of multicompartmental neurons
• Mean field models
• The Virtual Brain applications
• Multimodal models of brain dynamics
• Brain Digital Twins
• Simulations of brain dynamics
• Model validation with neurophysiological and neuroimaging data
• Application and prediction in clinical populations
• Brain-inspired model applications in artificial systems and robots
• Neuromorphic controllers

Are you interested in contributing a manuscript to this Research Topic?

Multiscale modeling of brain dynamics: development, validation, and applications in artificial systems or clinical populations