From Brain Models to Executable Digital Twins: Execution Semantics and Neuro-Neuromorphic Systems

2026-04-15

AI Summary

Current approaches to brain digital twins suffer from significant fragmentation across data pipelines, model types, temporal scales, and computational platforms, undermining semantic consistency throughout the execution pipeline. This work proposes a unified perspective of “executability under physical constraints” to establish a continuous execution framework spanning offline modeling, co-simulation, and online data assimilation, further extended into a neuro-inspired physical system paradigm that integrates biological and computational dynamics. By introducing execution semantics, the study emphasizes semantic interoperability, hybrid temporal correctness, and closed-loop validation safety, thereby transcending conventional accuracy-centric model evaluation. Grounded in modeling and simulation theory, it synthesizes data assimilation, neuromorphic computing, and runtime system techniques to construct a comparative framework for execution semantics across heterogeneous methodologies, advancing scalable, reproducible, and trustworthy brain digital twin systems.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Brain digital twins aim to provide faithful, individualized computational representations of brains as dynamical systems, enabling mechanistic understanding and supporting prediction of clinical interventions. Yet current approaches remain fragmented across data pipelines, model classes, temporal scales, and computing platforms, which prevents the preservation of execution semantics across the end-toend workflow. This survey introduces physically constrained executability as a unifying perspective for comparing approaches at the level of execution: whether an execution state is persistent, which events are permitted to update it (simulation, measurement, actuation), and how strongly execution is temporally and causally coupled to neurobiological dynamics. Building on modeling and simulation theory, I propose a taxonomy of execution regimes ranging from isolated offline models to coordinated co-simulation, to continuously executing digital twins sustained by online data assimilation, and ultimately to neuro-neuromorphic physical systems in which biological and computational dynamics are co-executed under shared physical constraints. The executability concept clarifies why accuracy alone is insufficient, and motivates an agenda centered on semantic interoperability, hybrid-time correctness, evaluation protocols, scalable reproducible workflows, and safe closed-loop validation. This survey adopts a systems and runtime-oriented perspective, enabling comparison of heterogeneous approaches based on their execution semantics rather than on model form or application domain alone.

Problem

• digital twins
• execution semantics
• neuro-neuromorphic systems
• semantic interoperability
• brain modeling

Innovation

• executability
• digital twins
• neuro-neuromorphic systems
• execution semantics
• data assimilation