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About

Jun Igarashi is a pioneering computational neuroscientist whose work bridges large-scale brain simulation and real-time robotics. His primary research areas include spiking neural network modeling of the cerebellum, real-time brain simulation on high-performance computing (HPC) systems, and embodied brain-body models for understanding motor control and learning. Igarashi’s most influential contribution is the development of the “Realtime cerebellum” model (2013, 83 citations), a large-scale spiking network of the cerebellum that runs in real time on a graphics processing unit (GPU)—a breakthrough enabling adaptive motor control simulations to keep pace with the physical world. He further advanced this work by implementing a cat-scale artificial cerebellum on energy-efficient PEZY-SC processors (2017, 22 citations), demonstrating that supercomputers like Shoubu can achieve real-time performance. Most recently, Igarashi led the “Embodied bidirectional simulation” project (2023, 7 citations), which distributed a spiking cortico-basal ganglia-cerebellar-thalamic brain model and a mouse musculoskeletal body across multiple computers, including the supercomputer Fugaku. This work represents a major step toward understanding how neural circuits generate behavior in realistic, closed-loop systems. His achievements highlight the potential of HPC for neuroscience, with implications for neurorobotics and brain-inspired computing.