Abstract
Here we present the Digital Brain (DB)—a platform for simulating spiking neuronal networks at the large neuron scale of the human brain on the basis of personalized magnetic resonance imaging data and biological constraints. The DB aims to reproduce both the resting state and certain aspects of the action of the human brain. An architecture with up to 86 billion neurons and 14,012 GPUs—including a two-level routing scheme between GPUs to accelerate spike transmission in up to 47.8 trillion neuronal synapses—was implemented as part of the simulations. We show that the DB can reproduce blood-oxygen-level-dependent signals of the resting state of the human brain with a high correlation coefficient, as well as interact with its perceptual input, as demonstrated in a visual task. These results indicate the feasibility of implementing a digital representation of the human brain, which can open the door to a broad range of potential applications.
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Data availability
The datasets and Source data that were used to establish and validate to the Digital Brain in this Brief Communication are publicly available34. All requests for further information of the datasets should be addressed to, and fulfilled by, our group: the DTB Consortium, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, via [email protected]. Source data are provided with this paper.
Code availability
Custom codes in Pytorch for PC and C++ for the HPC systems of our DB platform can be accessed via our GitHub profile (https://github.com/DTB-consortium/Digital_twin_brain-open) and Zenodo (https://doi.org/10.5281/zenodo.13995756)45. The code is also available as Supplementary Code to the manuscript. Note that our code strongly depends on the hardware and software conditions of our HPC system and may not run on other HPC systems. All requests for further information of code should be addressed to our group: the DTB Consortium, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, via [email protected].
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Acknowledgements
We thank T. W. Robbins, E. Rolls, K. Friston and D. Waxman for their helpful comments on the paper. The simulation was supported by Advanced Computing East China Sub-Centre. This work received support from the following sources: STI2030-Major Projects 2021ZD0200204, Shanghai Municipal Science and Technology Major Project (grant no. 2018SHZDZX01), Zhangjiang Lab, Shanghai Center for Brain Science and Brain-Inspired Technology, the 111 Project (grant no. B18015) and the National Natural Science Foundation of China (grant no. 62072111).
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W.L., Q.Z. and J.F. conceptualized the study. W.L., Q.Z. and J.F. designed the analytic approach. W.L., S.X., L.Z., J.W., X.D. and the DTB Consortium completed the investigation. S.X., L.Z. and the DTB Consortium helped with the visualization. N.X. and J.F. acquired funding. Q.Z. and J.F. assisted with project administration. W.L., S.X., L.Z., J.W. and the DTB Consortium wrote the paper. W.L., S.X., L.Z., J.W., X.D. and J.F. revised the first draft. All authors critically revised the paper.
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Nature Computational Science thanks James Aimone and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.
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Extended data
Extended Data Fig. 1 Preciseness of Simulation of the Euler–Maruyama method.
The comparison of the membrane potentials in a LIF neuronal network of 1000 neurons by the Euler–Maruyama method with time-step 1 msec (blue plot) and 0.001 msec (purple plot).
Source data
Extended Data Fig. 2 Digital brain (DB) in action (visual evaluation task).
The distributions of Pearson correlations between the empirical and assimilated BOLD signals at voxel-level in different brain parts were evaluated in the left panel. The box represents the interquartile range (IQR) from the 25th to the 75th percentile, with the median indicated by a line within the box. Whiskers extend to the minimum and maximum values, excluding outliers, providing a comprehensive overview of the data distribution. In the right panel, region-level correlations showed a positive association with the strength of structural connections (obtained from DWI) between the corresponding brain region to the input region (Pearson r = 0.678, p = 3.8e-11, df=120). Tests were two-sided, with no multiple comparison adjustments, yielding an effect size of 0.678, and a 95% CI of [0.624, 0.725] (right panel).
Source data
Extended Data Fig. 3 Framework of Hierarchical Bayesian Inference.
The hyperparameter layer presents the random walk to updating of the hyperparameter from (lambda (t)) to ({lambda }^{{prime} }). The parameter layer presents the sampling process of the parameter vector (theta (t)) from the hyperparameter by the sampling operator (Phi (bullet )) and being modified via the changes from (lambda (t)) to ({lambda }^{{prime} }), which gives (theta (t+1)). The computational model layer shows the evolution of the hidden state (x(t)) by iteratively computing the computational model (dot{x}=F(x,theta )), which influenced by the parameter vector (theta (t)). The experimental layer shows how the observation (y(t+1)), which is obtained from the hidden state (x(t+1)), is used to update the hidden state and the parameters, and in particular, to resample the hyperparameters from ({lambda }^{{prime} }) to (lambda (t+1)).
Supplementary information
Supplementary Information
Supplementary Sections 1–5, Figs. 1–8, Tables 1–5 and Algorithms 1–3.
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Supplementary Code
Custom code for the simulation and assimilation of the Digital Brain, also available at https://doi.org/10.5281/zenodo.13995756.
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Source Data Extended Data Fig. 1
The numerical source data for Extended Data Fig. 1.
Source Data Extended Data Fig. 2
The numerical source data for Extended Data Fig. 2.
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Lu, W., Du, X., Wang, J. et al. Simulation and assimilation of the digital human brain.
Nat Comput Sci (2024). https://doi.org/10.1038/s43588-024-00731-3
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Received: 24 April 2024
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Accepted: 30 October 2024
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Published: 19 December 2024
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DOI: https://doi.org/10.1038/s43588-024-00731-3