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

TL;DR

This survey introduces a unified framework for evaluating brain digital twins based on execution semantics, emphasizing interoperability, correctness, and validation across diverse neurocomputational systems.

Contribution

It proposes a taxonomy of execution regimes and a unifying perspective of physically constrained executability for comparing neuro-neuromorphic approaches.

Findings

Introduces a taxonomy of execution regimes from offline models to physical systems.

Highlights the importance of semantic interoperability and hybrid-time correctness.

Provides a systems-oriented perspective for comparing heterogeneous approaches.

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.