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arxiv: 2604.13574 · v1 · submitted 2026-04-15 · 💻 cs.CE · cs.NE· cs.SE· q-bio.NC

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

Alexandre Muzy (ILLS) This is my paper

Pith reviewed 2026-05-10 12:46 UTC · model grok-4.3

classification 💻 cs.CE cs.NEcs.SEq-bio.NC
keywords brain digital twinsexecution semanticsphysically constrained executabilityneuro-neuromorphic systemsdigital twinsco-simulationonline data assimilationsimulation semantics
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The pith

Brain digital twins achieve fidelity through preserved execution semantics under physical constraints rather than model accuracy alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that brain digital twins, intended as faithful individualized representations of brains as dynamical systems, remain fragmented across data pipelines, model classes, temporal scales, and platforms. This fragmentation prevents preservation of execution semantics from model creation through to runtime use. Physically constrained executability is introduced as the unifying perspective, evaluating whether an execution state persists, which events such as simulation, measurement, or actuation may update it, and how tightly the execution couples temporally and causally to actual neurobiological dynamics. The result is a taxonomy progressing from isolated offline models to coordinated co-simulation, continuously executing twins sustained by online data assimilation, and ultimately neuro-neuromorphic physical systems in which biological and computational dynamics co-execute under shared physical constraints. A reader would care because this reframes the path to mechanistic understanding and clinical prediction around semantic interoperability instead of accuracy in isolation.

Core claim

Brain digital twins aim to provide faithful, individualized computational representations of brains as dynamical systems but stay fragmented, which blocks preservation of execution semantics across the end-to-end workflow. Physically constrained executability supplies the unifying view by checking persistence of the execution state, the events permitted to update it, and the strength of temporal and causal coupling to neurobiological dynamics. This view supports a taxonomy of execution regimes ranging from isolated offline models through coordinated co-simulation and online data-assimilating twins to neuro-neuromorphic physical systems where biological and computational dynamics share the sa

What carries the argument

Physically constrained executability, the mechanism that compares approaches by persistence of execution state, permitted updating events, and degree of temporal-causal coupling to neurobiological dynamics.

If this is right

  • Approaches become comparable and interoperable by execution regime rather than by model form or application domain.
  • Neuro-neuromorphic systems allow biological and computational dynamics to co-execute under shared physical constraints.
  • Evaluation protocols must incorporate hybrid-time correctness to verify faithful dynamics.
  • Scalable reproducible workflows become necessary to move from models to validated digital twins.
  • Safe closed-loop validation for clinical interventions becomes feasible once semantics are preserved.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same execution-regime lens could organize digital-twin development in other fragmented domains such as climate or manufacturing systems.
  • Neuro-neuromorphic co-execution may enable lower-latency real-time modeling than conventional hardware.
  • Applying the taxonomy to existing brain models could identify which regimes best support individualized clinical predictions.
  • The runtime-oriented perspective connects naturally to verification techniques already used in cyber-physical systems.

Load-bearing premise

Preserving execution semantics across the full workflow is the primary barrier to faithful individualized brain digital twins.

What would settle it

A controlled test in which two brain models with matched accuracy but differing execution regimes—one with persistent state and strong coupling, the other isolated offline—are evaluated side-by-side for success in cross-platform interoperability or clinical outcome prediction.

Figures

Figures reproduced from arXiv: 2604.13574 by Alexandre Muzy (ILLS).

Figure 1
Figure 1. 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 mode… view at source ↗
Figure 2
Figure 2. 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 repre… view at source ↗
Figure 3
Figure 3. 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 couple… view at source ↗
Figure 4
Figure 4. Figure 4: Brain measurements supporting brain digital twins. Multimodal measurements provide complementary access to brain structure, dynamics, and function across spatial and tem￾poral scales. Structural imaging (MRI, diffusion MRI) constrains anatomy and connectivity. Func￾tional imaging (fMRI, fNIRS, PET) captures large-scale activity and metabolism. Electrophysiological recordings (EEG, MEG, ECoG, SEEG) provide … view at source ↗
Figure 5
Figure 5. Figure 5: Emergent execution granularity under physical execution constraints. This illus￾tration 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 rep￾resentations and execution unit… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript surveys brain digital twins as computational representations of brains as dynamical systems. It argues that fragmentation across data pipelines, model classes, temporal scales, and platforms prevents preservation of execution semantics in end-to-end workflows. The paper introduces 'physically constrained executability' as a unifying lens—focusing on persistence of execution state, permitted update events (simulation/measurement/actuation), and temporal/causal coupling to neurobiological dynamics—and proposes a taxonomy of execution regimes from isolated offline models through coordinated co-simulation and online data-assimilation twins to neuro-neuromorphic physical systems. It motivates an agenda on semantic interoperability, hybrid-time correctness, evaluation protocols, reproducible workflows, and safe closed-loop validation, adopting a systems/runtime perspective rather than model-form or domain-based comparison.

Significance. If the taxonomy and executability perspective prove robust in application, the work could provide a valuable organizing framework for comparing and integrating heterogeneous brain modeling approaches in computational neuroscience and digital-twin engineering. By shifting emphasis from accuracy alone to execution semantics and interoperability, it addresses a recognized fragmentation barrier and supplies concrete research directions (e.g., hybrid-time correctness and closed-loop validation protocols) that could accelerate progress toward faithful, individualized twins. The conceptual, non-empirical nature is appropriate for a survey and avoids overclaiming demonstration of solutions.

major comments (1)
  1. The central claim that the proposed taxonomy of execution regimes will enable semantic interoperability where model accuracy alone has failed is load-bearing for the unifying contribution. However, the manuscript does not appear to include systematic mappings of representative existing brain-modeling approaches (e.g., specific papers or model classes) onto the regimes, which would be required to substantiate exhaustiveness and practical utility. Without such concrete applications, it remains unclear whether gaps in coverage undermine the taxonomy's ability to unify the field.
minor comments (2)
  1. The abstract states that the survey 'builds on modeling and simulation theory' yet does not explicitly list the key prior frameworks or theorems invoked; adding a short dedicated subsection or table citing the foundational references would improve traceability of the taxonomy construction.
  2. The phrase 'end-toend workflow' in the abstract is missing a hyphen and should be corrected to 'end-to-end workflow' for consistency with standard technical writing.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and positive review, including the recommendation for minor revision. The feedback identifies a valuable opportunity to strengthen the manuscript's substantiation of the proposed taxonomy. We address the major comment below and will update the manuscript accordingly.

read point-by-point responses

  1. Referee: The central claim that the proposed taxonomy of execution regimes will enable semantic interoperability where model accuracy alone has failed is load-bearing for the unifying contribution. However, the manuscript does not appear to include systematic mappings of representative existing brain-modeling approaches (e.g., specific papers or model classes) onto the regimes, which would be required to substantiate exhaustiveness and practical utility. Without such concrete applications, it remains unclear whether gaps in coverage undermine the taxonomy's ability to unify the field.

    Authors: We agree that systematic mappings of representative approaches would strengthen the demonstration of the taxonomy's coverage and practical utility for enabling semantic interoperability. The manuscript introduces physically constrained executability as a conceptual framework grounded in modeling and simulation theory, with illustrative references to existing work across regimes. To address the concern directly, the revised version will include a new dedicated subsection that maps key brain-modeling approaches—such as offline large-scale spiking models (e.g., from the Blue Brain Project and Allen Institute), co-simulation platforms (e.g., TVB-NEST integrations), online data-assimilation twins in clinical neuroscience, and neuro-neuromorphic hardware systems—onto the execution regimes. These mappings will explicitly relate each example to persistence of execution state, permitted update events, and temporal/causal coupling, thereby clarifying how the taxonomy reveals interoperability gaps beyond accuracy metrics alone and supports the proposed research agenda. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a conceptual survey introducing 'physically constrained executability' as a unifying perspective and proposing a taxonomy of execution regimes. It contains no mathematical derivations, equations, fitted parameters, or predictions that reduce to inputs by construction. The central claims rest on definitional framing and cited modeling theory without self-referential reductions, load-bearing self-citations forming a circular chain, or renaming of known results as novel derivations. The work explicitly positions itself as motivating an agenda rather than demonstrating a closed-form result from its own premises.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that fragmentation in execution semantics is the key obstacle, with the invented concept of physically constrained executability introduced to unify approaches without independent falsifiable evidence beyond the proposal itself.

axioms (1)
  • domain assumption Current approaches to brain digital twins remain fragmented across data pipelines, model classes, temporal scales, and computing platforms, preventing preservation of execution semantics.
    Directly stated in the abstract as the motivating problem.
invented entities (1)
  • physically constrained executability no independent evidence
    purpose: Unifying perspective and taxonomy for comparing brain model execution semantics
    Introduced in the abstract as a new lens; no independent evidence or falsifiable prediction provided.

pith-pipeline@v0.9.0 · 5526 in / 1291 out tokens · 30196 ms · 2026-05-10T12:46:44.075829+00:00 · methodology

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Reference graph

Works this paper leans on

53 extracted references · 53 canonical work pages

  1. [1]

    Jakob Zimmermann, Anastasia Popov, Timothée Proix, and Viktor K. Jirsa. Brain digital twins: A computational revolution in clinical neuroscience.Frontiers in Neuroscience, 16:943754, 2022

    work page 2022

  2. [2]

    Wang, Paul Triebkorn, Martin Breyton, Borana Dollomaja, Jean-Didier Lemarechal, Spase Petkoski, Pierpaolo Sorrentino, Damien Depannemaecker, Meysam Hashemi, and Viktor K

    Huifang E. Wang, Paul Triebkorn, Martin Breyton, Borana Dollomaja, Jean-Didier Lemarechal, Spase Petkoski, Pierpaolo Sorrentino, Damien Depannemaecker, Meysam Hashemi, and Viktor K. Jirsa. Virtual brain twins: from basic neuroscience to clinical use. National Science Review, 11(5):nwae079, 2024

    work page 2024

  3. [3]

    The digital twin brain: A bridge between biological and artificial intelligence.Intelligent Computing, 2(3):0055, 2023

    Jing Xiong, Liyuan Zhang, Yifan Wang, Liang Chen, and Lin Zhang. The digital twin brain: A bridge between biological and artificial intelligence.Intelligent Computing, 2(3):0055, 2023

    work page 2023

  4. [4]

    Fekonja, Robert Schenk, Anna Schröder, Rosario Tomasello, Samo Tomšič, and Thomas Picht

    Ledina S. Fekonja, Robert Schenk, Anna Schröder, Rosario Tomasello, Samo Tomšič, and Thomas Picht. The digital twin in neuroscience: from theory to tailored therapy.Frontiers in Neuroscience, 18:1454856, 2024

    work page 2024

  5. [5]

    The human brain project—synergy between neuroscience, computing, informatics, and brain- inspired technologies.PLoS Biology, 17(7):e3000344, 2019

    Katrin Amunts, Christoph Ebell, Jens Muller, Martin Telefont, Alois Knoll, and Thomas Lippert. The human brain project—synergy between neuroscience, computing, informatics, and brain- inspired technologies.PLoS Biology, 17(7):e3000344, 2019. 12

    work page 2019

  6. [6]

    The human brain project: A blueprint for brain research and technology.Fron- tiers in Neuroscience, 6:89, 2012

    Henry Markram. The human brain project: A blueprint for brain research and technology.Fron- tiers in Neuroscience, 6:89, 2012

    work page 2012

  7. [7]

    The coming decade of digital brain research: A vision for large-scale integration of neuroimaging, histology, and behaviour.Imaging Neuroscience, 2:1–16, 2024

    Katrin Amunts, Alexander Schaefer, Michael Schirner, et al. The coming decade of digital brain research: A vision for large-scale integration of neuroimaging, histology, and behaviour.Imaging Neuroscience, 2:1–16, 2024

    work page 2024

  8. [8]

    Academic Press, 2018

    Bernard P Zeigler, Alexandre Muzy, and Ernesto Kofman.Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations. Academic Press, 2018

    work page 2018

  9. [9]

    Scalability of large neural network simulations via activity tracking with time asynchrony and procedural con- nectivity

    Cyrille Mascart, Gilles Scarella, Patricia Reynaud-Bouret, and Alexandre Muzy. Scalability of large neural network simulations via activity tracking with time asynchrony and procedural con- nectivity. Neural Computation, 34(9):1915–1943, 2022

    work page 1915

  10. [10]

    Delay neural networks (DeNN) for exploiting temporal information in event-based datasets.Communications Engineering (Nature), 2025

    Alban Gattepaille and Alexandre Muzy. Delay neural networks (DeNN) for exploiting temporal information in event-based datasets.Communications Engineering (Nature), 2025. Accepted for publication

    work page 2025

  11. [11]

    Inferring time-varying internal models of agents through dynamic structure learning.bioRxiv, pages 2024–09, 2024

    Ashwin James, Ingrid Bethus, and Alexandre Muzy. Inferring time-varying internal models of agents through dynamic structure learning.bioRxiv, pages 2024–09, 2024

    work page 2024

  12. [12]

    Widge, and Kai J

    Carlos Herrera, Alik S. Widge, and Kai J. Miller. Closed-loop adaptive brain stimulation: Emerg- ing methods and clinical applications.Brain Stimulation, 15(3):580–596, 2022

    work page 2022

  13. [13]

    Valdes-Sosa, and Sylvain Baillet

    Guiomar Niso, Kristian Madsen, Pedro A. Valdes-Sosa, and Sylvain Baillet. Brain stimula- tion modeling and validation: Towards personalized neurostimulation therapies. NeuroImage, 271:120002, 2023

    work page 2023

  14. [14]

    Andreas Horn and Michael D. Fox. Opportunities of connectomic deep brain stimulation.Nature Reviews Neurology, 16(6):337–350, 2020

    work page 2020

  15. [15]

    Yu Huang, Abhishek Datta, Marom Bikson, and Lucas C. Parra. Realistic volumetric-approach to simulate transcranial electric stimulation—ROAST—a fully-automated open-source pipeline. Brain Stimulation, 12(3):665–667, 2019

    work page 2019

  16. [16]

    Reconstruction and simulation of neocortical microcircuitry.Cell, 163(2):456–492, 2015

    Henry Markram, Eilif Muller, Srikanth Ramaswamy, et al. Reconstruction and simulation of neocortical microcircuitry.Cell, 163(2):456–492, 2015

    work page 2015

  17. [17]

    Falcon, Aloys Tucholka, et al

    Murat Demirtaş, Miguel I. Falcon, Aloys Tucholka, et al. A whole-brain computational modeling approach to explain cognitive deficits in Alzheimer’s disease.NeuroImage, 150:249–261, 2017

    work page 2017

  18. [18]

    Aponte, Michael Schirner, Viktor K

    Erick A. Aponte, Michael Schirner, Viktor K. Jirsa, Petra Ritter, and Gustavo Deco. The im- pact of structural connectivity and recurrent neural dynamics on human resting-state functional connectivity. Frontiers in Neuroscience, 17:1163814, 2023

    work page 2023

  19. [19]

    Jirsa, Timothée Proix, Dionysios Perdikis, et al

    Viktor K. Jirsa, Timothée Proix, Dionysios Perdikis, et al. The virtual epileptic patient: Individ- ualized whole-brain models of epilepsy spread.NeuroImage, 145:377–388, 2017

    work page 2017

  20. [20]

    McIntosh, Petra Ritter, and Viktor K

    Michael Schirner, Silvan Rothmeier, Anthony R. McIntosh, Petra Ritter, and Viktor K. Jirsa. Brain simulation as a cloud service: The virtual brain on EBRAINS.NeuroImage, 251:119010, 2022

    work page 2022

  21. [21]

    Timothée Proix, Fabrice Bartolomei, Maxime Guye, and Viktor K. Jirsa. Individualized virtual brain models for epilepsy surgery planning.Current Opinion in Neurology, 31(2):140–147, 2018

    work page 2018

  22. [22]

    McIntosh and James M

    Anthony R. McIntosh and James M. Shine. Network modeling of Parkinson’s disease: Mechanisms and clinical insights.Progress in Brain Research, 261:241–260, 2021. 13

    work page 2021

  23. [23]

    McIntosh, Petra Ritter, and Viktor K

    Michael Schirner, Silvan Rothmeier, Anthony R. McIntosh, Petra Ritter, and Viktor K. Jirsa. Constructing subject-specific virtual brains from multimodal neuroimaging data. NeuroImage, 117:343–357, 2015

    work page 2015

  24. [24]

    Friston, Vladimir Litvak, Ashwini Oswal, et al

    Karl J. Friston, Vladimir Litvak, Ashwini Oswal, et al. Dynamic causal modeling revisited. NeuroImage, 199:730–744, 2019

    work page 2019

  25. [25]

    Moran, Maja Symmonds, Raymond J

    Rosalyn J. Moran, Maja Symmonds, Raymond J. Dolan, and Karl J. Friston. The brain ages optimally to model its environment: evidence from DCM of pharmacological fMRI.NeuroImage, 94:103–115, 2014

    work page 2014

  26. [26]

    Penny, Klaas E

    Will D. Penny, Klaas E. Stephan, Jean Daunizeau, et al. Comparing families of dynamic causal models. PLoS Computational Biology, 6(3):e1000709, 2010

    work page 2010

  27. [27]

    Friston, Joshua Kahan, Bharat Biswal, and Adeel Razi

    Karl J. Friston, Joshua Kahan, Bharat Biswal, and Adeel Razi. A DCM for resting state fMRI. NeuroImage, 94:396–407, 2014

    work page 2014

  28. [28]

    Fabian Gilbert, Timothée Proix, Fabrice Bartolomei, and Viktor K. Jirsa. EEG-informed large- scale brain network modeling in epilepsy: linking neurophysiology and simulation.Frontiers in Neurology, 12:725569, 2021

    work page 2021

  29. [29]

    Vattikonda, Viktor Sip, et al

    Mina Hashemi, Ashwin N. Vattikonda, Viktor Sip, et al. Personalized hybrid brain models for data fusion and simulation.Frontiers in Neuroscience, 14:105, 2020

    work page 2020

  30. [30]

    Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.Brain, 140(2):319–332, 2017

    Nishant Sinha, Justin Dauwels, Marcus Kaiser, et al. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.Brain, 140(2):319–332, 2017

    work page 2017

  31. [31]

    Optimization of surgical inter- vention outside the epileptogenic zone in the virtual epileptic patient (VEP).PLOS Computational Biology, 15(6):e1007051, 2019

    Shanshan An, Fabrice Bartolomei, Maxime Guye, and Viktor Jirsa. Optimization of surgical inter- vention outside the epileptogenic zone in the virtual epileptic patient (VEP).PLOS Computational Biology, 15(6):e1007051, 2019

    work page 2019

  32. [32]

    The human brain project.Scientific American, 306(6):50–55, 2012

    Henry Markram. The human brain project.Scientific American, 306(6):50–55, 2012

    work page 2012

  33. [33]

    Thehumanbrainproject: Creatingaeuropean research infrastructure to decode the human brain.Neuron, 92(3):574–581, 2016

    KatrinAmunts, CorneliusEbell, JeanMuller, etal. Thehumanbrainproject: Creatingaeuropean research infrastructure to decode the human brain.Neuron, 92(3):574–581, 2016

    work page 2016

  34. [34]

    Glasser, Timothy S

    Matthew F. Glasser, Timothy S. Coalson, Emma C. Robinson, et al. A multi-modal parcellation of human cerebral cortex.Nature, 536:171–178, 2016

    work page 2016

  35. [35]

    Insel, Story C

    Thomas R. Insel, Story C. Landis, and Francis S. Collins. The NIH BRAIN initiative.Science, 340(6133):687–688, 2013

    work page 2013

  36. [36]

    Bargmann and William T

    Cornelia I. Bargmann and William T. Newsome. A vision for the BRAIN initiative.Neuron, 84(3):529–533, 2014

    work page 2014

  37. [37]

    Wilkinson et al

    Mark D. Wilkinson et al. The FAIR guiding principles for scientific data management and stew- ardship. Scientific Data, 3:160018, 2016

    work page 2016

  38. [38]

    Gorgolewski, Tibor Auer, Vince D

    Krzysztof J. Gorgolewski, Tibor Auer, Vince D. Calhoun, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3:160044, 2016

    work page 2016

  39. [39]

    Gorgolewski, Elizabeth Bock, et al

    Guiomar Niso, Krzysztof J. Gorgolewski, Elizabeth Bock, et al. MEG-BIDS, the brain imaging data structure extended to magnetoencephalography.Scientific Data, 5:180110, 2018

    work page 2018

  40. [40]

    Pernet, Stefan Appelhoff, Krzysztof J

    Cyril R. Pernet, Stefan Appelhoff, Krzysztof J. Gorgolewski, et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography.Scientific Data, 6(1):103, 2019

    work page 2019

  41. [41]

    Markiewicz, Ross W

    Oscar Esteban, Christopher J. Markiewicz, Ross W. Blair, et al. fMRIPrep: a robust preprocessing pipeline for functional MRI.Nature Methods, 16(1):111–116, 2019. 14

    work page 2019

  42. [42]

    Cook, Xiaosong He, et al

    Matthew Cieslak, Philip A. Cook, Xiaosong He, et al. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data.Nature Methods, 18(7):775–778, 2021

    work page 2021

  43. [43]

    Towards spike-based machine intelli- gence with neuromorphic computing.Nature, 575:607–617, 2019

    Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda. Towards spike-based machine intelli- gence with neuromorphic computing.Nature, 575:607–617, 2019

    work page 2019

  44. [44]

    Falcon, Jeffrey D

    Miguel I. Falcon, Jeffrey D. Riley, Viktor K. Jirsa, and Anthony R. McIntosh. Digital twin brain models: Towards real-time clinical decision support.Frontiers in Digital Health, 5:1223345, 2023

    work page 2023

  45. [45]

    Iterative specification as a modeling and simulation formalism for I/O general systems.IEEE Systems Journal, 12(3):2982–2993, 2017

    Alexandre Muzy, Bernard P Zeigler, and Franck Grammont. Iterative specification as a modeling and simulation formalism for I/O general systems.IEEE Systems Journal, 12(3):2982–2993, 2017

    work page 2017

  46. [46]

    Exact simulation of integrate-and-fire models with synaptic conductances.Neural Computation, 19(10):2604–2609, 2007

    Romain Brette. Exact simulation of integrate-and-fire models with synaptic conductances.Neural Computation, 19(10):2604–2609, 2007

    work page 2007

  47. [47]

    Memory and information processing in neuromorphic sys- tems

    Giacomo Indiveri and Shih-Chii Liu. Memory and information processing in neuromorphic sys- tems. Proceedings of the IEEE, 103(8):1379–1397, 2015

    work page 2015

  48. [48]

    Merolla, John V

    Paul A. Merolla, John V. Arthur, Rodrigo Alvarez-Icaza, et al. A million spiking-neuron integrated circuit with a scalable communication network.Science, 345(6197):668–673, 2014

    work page 2014

  49. [49]

    Loihi: A neuromorphic manycore processor with on-chip learning.IEEE Micro, 38(1):82–99, 2018

    Mike Davies, Narayan Srinivasa, Tsung-Han Lin, et al. Loihi: A neuromorphic manycore processor with on-chip learning.IEEE Micro, 38(1):82–99, 2018

    work page 2018

  50. [50]

    Furber, Francesco Galluppi, Steve Temple, and Luis A

    Steve B. Furber, Francesco Galluppi, Steve Temple, and Luis A. Plana. The SpiNNaker project. Proceedings of the IEEE, 102(5):652–665, 2014

    work page 2014

  51. [51]

    The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity.Frontiers in Neuroscience, 16:795876, 2022

    Christian Pehle, Sebastian Billaudelle, Benjamin Cramer, Jakob Kaiser, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Andreas Leibfried, Eric Müller, and Johannes Schemmel. The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity.Frontiers in Neuroscience, 16:795876, 2022

    work page 2022

  52. [52]

    Challenges and opportunities for digital twins in precision medicine

    Manlio De Domenico et al. Challenges and opportunities for digital twins in precision medicine. npj Digital Medicine, 8:37, 2025

    work page 2025

  53. [53]

    Calabro, et al

    Scott Marek, Brenden Tervo-Clemmens, Finnegan J. Calabro, et al. Reproducible brain-wide association studies require thousands of individuals.Nature, 603:654–660, 2022. 15

    work page 2022