Realistic multi-scale modeling of neural circuit dynamics - Volume II

We are pleased to announce this Volume II Research Topic, building upon the impactful contributions and insightful discussions generated by the first Research Topic in this series - 'Realistic Multi-Scale Modeling of Neural Circuit Dynamics'. Neural circuit dynamics remain profoundly complex, underpinned by intricate interactions spanning molecular, cellular, and network scales. While significant advancements in computational neuroscience have enabled the development of increasingly detailed biophysical models, the fundamental challenge persists: effectively bridging these disparate scales to achieve truly comprehensive and predictive simulations. This ongoing endeavor is crucial for advancing our understanding of brain function and dysfunction, necessitating continued exploration and innovation in multi-scale modeling approaches.

Building on the foundation laid by Volume I, this Research Topic aims to further refine and expand the development of multi-scale models that seamlessly integrate molecular, cellular, and network-level data for simulating neural circuit dynamics with unprecedented realism. The primary objective is to move beyond initial bridging efforts, fostering models that not only closely align with empirical observations but also offer enhanced predictive capabilities for understanding normal brain function, pathological states, and responses to interventions. We seek contributions that advance the methodologies and applications of these models, pushing the boundaries of what is possible in computational neuroscience to provide deeper insights into the mechanisms underlying neurological processes and to identify novel therapeutic avenues. This involves addressing the complexities of dynamic interactions across scales and rigorously validating models against diverse experimental datasets.

This Research Topic continues to define and expand the capabilities of multi-scale simulation models, inviting contributions that drive further advancements in computational neuroscience. We encourage submissions that present novel methodologies, insightful applications, and rigorous validations. We specifically welcome articles addressing themes such as:
- Advanced multi-scale modeling techniques and novel algorithmic developments
- Enhanced integration of omics data (genomic, proteomic, metabolomic) and multi-synaptic plasticity into large-scale brain models
- Innovative applications of machine learning, including explainable AI, for model optimization, precision, and scalability
- Rigorous cross-validation of multi-scale models with diverse and high-resolution experimental neuroscience data
- In-depth case studies exploring specific neural circuits, their dynamic functions, or their role in neurological disorders and therapeutic interventions