Abstract—In this work, we present a computing platform
named digital twin brain (DTB) that can simulate spiking
neuronal networks of the whole human brain scale and more
importantly, a personalized biological brain structure. In comparison to most brain simulations with a homogeneous global
structure, we highlight that the sparseness, couplingness and
heterogeneity in the sMRI, DTI and PET data of the brain has
an essential impact on the efficiency of brain simulation, which
is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access-intensive computing systems rather than computation-intensive.
We utilize a number of optimization techniques to balance and
integrate the computation loads and communication traffics from
the heterogeneous biological structure to the general GPU-based
HPC and achieve leading simulation performance for the whole
human brain-scaled spiking neuronal networks. On the other
hand, the biological structure, equipped with a mesoscopic data
assimilation, enables the DTB to investigate brain cognitive function by a reverse-engineering method, which is demonstrated by a
digital experiment of visual evaluation on the DTB. Furthermore,
we believe that the developing DTB will be a promising powerful
platform for a large of research orients including brain-inspired
intelligence, rain disease medicine and brain-machine interface.