cs.CEApr 15, 2026

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

Authors: Alexandre Muzy

Organizations: ILLS · International Laboratory on Learning Systems (ILLS) CNRS – McGill – ETS – MILA – Université Paris-Saclay

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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.

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Figure 1: From fragmented brain models to executable neuro–neuromorphic systems. (A) Current brain digital twin approaches are fragmented across data processing, modeling, simulation, and neuroscientific/clinical interpretation. Each of these steps is typically organized as separate and offline pipelines. They are executed independently, with limited temporal coupling and no persistent feedback. This results in models that are biologically informed but not continuously executable. (B) Executable brain digital twins correspond to systems in which computational models are embedded within ongoing simulations coupled to data assimilation processes. Brain observations are integrated online to update model states and parameters during execution, enabling sustained temporal alignment between biological and digital dynamics. A key distinction at this stage is the presence of a persistent, shared execution state that is maintained over time and updated online through continuous observation and model evolution. (C) At the highest level of executability, neuro–neuromorphic systems co-execute biological and digital dynamics as a coupled system.
Figure 2: Executability as state evolution across coupled time scales. Executable brain digital twins maintain a coherent execution state that evolves across multiple, coupled temporal regimes. Level 1 corresponds to fast neurophysical dynamics of the biological brain, including spiking activity, field potentials, oscillations, and behavior, which evolve in continuous time and generate discrete events. Level 2 represents the executing state of the brain digital twin, defined by the internal state variables of the computational model during simulation and inference. This state evolves at an intermediate time scale and is continuously synchronized with brain activity through online observations and data assimilation. Level 3 captures slow model adaptation processes, including parameter calibration and structural updates, which integrate accumulated mismatches over time. Together, these levels define a hybrid temporal semantics in which continuous dynamics, discrete updates, and event-driven corrections coexist, enabling sustained execution rather than episodic recalibration of the digital twin.
Figure 3: Progressive executability of brain digital twins. The figure illustrates four execution regimes that characterize how brain digital twin approaches evolve from isolated computational models to fully embedded neuro–neuromorphic physical systems. Level I corresponds to disciplinary models executed offline in isolation. Level II captures co-simulation frameworks that enable the coordinated execution of coupled heterogeneous models without persistent data assimilation. Level III represents executable brain digital twins that sustain continuous execution through online data assimilation and state adaptation, while remaining observational. Level IV denotes neuro–neuromorphic physical systems, in which biological and digital dynamics are co-executed under physically constrained execution. Together, these levels reflect increasing degrees of executability, integration, and causality.
Figure 4: Brain measurements supporting brain digital twins. Multimodal measurements provide complementary access to brain structure, dynamics, and function across spatial and temporal scales. Structural imaging (MRI, diffusion MRI) constrains anatomy and connectivity. Functional imaging (fMRI, fNIRS, PET) captures large-scale activity and metabolism. Electrophysiological recordings (EEG, MEG, ECoG, SEEG) provide fast temporal sampling of neural dynamics; molecular and histological data refine cellular and receptor-level properties. Together, these measurements ground the construction, personalization, and validation of brain digital twins.
Figure 5: Emergent execution granularity under physical execution constraints. This illustration compares major computational execution substrates with progressively finer representations of the same biological brain. From left to right, increasing physical execution constraints—such as bounded latency, causal coupling, and event-level interaction—impose increasingly fine-grained representations and execution units. CPU-based platforms are associated with coarse-grained brain representations, depicted as large colored regions. GPU-based platforms support finer-grained representations, illustrated by denser color patterns reflecting massively parallel processing units. Neuromorphic supercomputers (e.g., SpiNNaker-like systems) correspond to distributed representations composed of interacting neuronal groups. Neuromorphic chips operate at the finest execution granularity. Overall, the figure highlights that representational and execution granularity do not arise as independent design choices, but emerge as a direct consequence of the physical constraints under which computation must be executed.
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