Cross-level computational integration: Linking molecular, cellular, and systems dynamics in unified brain models

The human brain functions through a complex hierarchy of interacting scales, for example, the stochastic behavior of individual ion channels, and the emergent oscillations of large-scale neural systems. Traditionally, computational neuroscience has operated within specialized silos: molecular modeling focuses on biochemical signaling, cellular modeling on electrophysiology, and systems modeling on network connectivity and behavior. However, this limits our ability to understand how microscopic perturbations such as genetic mutations or pharmacological interventions, propagate through the hierarchy to alter cognitive function. Recent advancements in high-performance computing, data-driven modeling, and hybrid simulation frameworks now provide a unique opportunity to bridge these gaps. By combining these disparate levels, we can move toward a holistic "unified brain model" that captures the causal links between biological mechanisms and computational outcomes.

The primary objective of this Research Topic is to address the "integration gap" in computational neuroscience. Currently, most models sacrifice biological detail for computational efficiency or focus on high-fidelity biophysics at the expense of network-level context, and therefore, this Research Topic aims to foster the development of multiscale frameworks that explicitly link molecular, cellular, and systems dynamics.

We seek to answer critical questions: How do intracellular signaling cascades modulate synaptic plasticity to reshape circuit-wide information processing? How do specific ion channel kinetics contribute to the stability or transition of global brain states, such as sleep-wake cycles or seizure activity? To achieve this, we encourage contributions that showcase co-simulation techniques, reduced-order modeling that retains biological relevance, and theoretical paradigms that explain cross-scale emergence. Ultimately, our goal is to provide a platform for research that moves from descriptive single-level modeling toward predictive, unified architectures.

We welcome papers that contribute to the unification of brain models across different levels of abstraction. We particularly encourage submissions that emphasize "upward" or "downward" causation across scales.

Specific themes of interest include, but are not limited to:
- Multiscale Software & Frameworks: Development of tools and APIs for the co-simulation of biochemical and electrophysiological models.
- Biophysical Network Modeling: Studies linking molecular-level drug actions or genetic variations to changes in circuit-level oscillations and behavior.
- Plasticity and Homeostasis: Computational models exploring how molecular pathways regulate synaptic and intrinsic plasticity within larger systems.
- Neuroinformatics and Data Integration: Strategies for mapping high-resolution "omics" data onto functional systems-level models.
- Pathophysiology across Scales: Unified models of neurological disorders that bridge molecular pathology with clinical phenotypes.