2 research outputs found
Digital Twin Brain: a simulation and assimilation platform for whole human brain
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-inspiredintelligence, rain disease medicine and
brain-machine interface.Comment: 12 pages, 11 figure
DTBVis: An interactive visual comparison system for digital twin brain and human brain
The digital twin brain (DTB) computing model from brain-inspired computing research is an emerging artificial intelligence technique, which is realized by a computational modeling approach of hardware and software. It can achieve various cognitive abilities and their synergistic mechanisms in a manner similar to the human brain. Given that the task of the DTB is to simulate the functions of the human brain, comparing the similarities and differences between the two is crucial. However, the visualization study of the DTB is still under-researched. Moreover, the complexity of the datasets (multilevel spatiotemporal granularity and different types of comparison tasks) presents new challenges to the analysis and exploration of visualization. Therefore, in this study, we proposed DTBVis, a visual analytics system that supports comparison tasks for the DTB. DTBVis supports iterative explorations from different levels and at different granularities. Combined with automatic similarity recommendation, and high-dimensional exploration, DTBVis can assist experts in understanding the similarities and differences between the DTB and the human brain, thus helping them adjust their model and enhance its functionality. The highest level of DTBVis shows an overview of the datasets from the brain, which is used for comparison and exploration of the function and structure of the DTB and the human brain. The medium level is used for the comparison and exploration of a designated brain region. The low level can analyze a designated brain voxel. We worked closely with experts of brain science and held regular seminars with them. Feedback from the experts indicates that our approach helps them conduct comparative studies of the DTB and human brain and make modeling adjustments of the DTB through intuitive visual comparisons and interactive explorations