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A whole-brain simulation allows us to investigate all interactions among neurons in the brain to understand the mechanisms of information processing and brain diseases. However, the whole-brain simulation has not been realized so far due to a lack of physiological data and computational resources. The supercomputer Fugaku is estimated to perform the whole-brain simulation on a human-scale due to its computational performance. To realize the whole-brain simulation and understand the brain's mechanism, we have worked in Post-K exploratory challenge 4-1 and started a new project of the Fugaku since April 2020. In the projects, we constructed a minimum unit of mammalian brain model consisting of the cerebral cortex, basal ganglia, cerebellum, and thalamus, which reproduced resting-state neural activity. We also have been developing an in-house neural network simulator, MONET, that can utilize the wide SIMD and many cores of A64FX of the Fugaku. The MONET simulator simulated models of the cerebral cortex with 5 billion neurons and cerebellum with 64 billion on the K computer. A recent experiment showed that MONET realized 40 times the more excellent computational performance per compute nodes for simulations of the cerebral cortex and cerebellum on the Fugaku evaluation environment III than the K computer. These results suggest that the full system of the Fugaku may realize whole-brain simulation and contribute to an understanding of the brain.