Understanding the brain remains a major scientific challenge due to its complex structure and function. Unlike artificial neural networks, the biological brain features diverse biophysical properties essential to its function. Building an accurate digital replica using extensive anatomical and physiological data, which are available in standardized public databases, has emerged as a promising approach. In this study, a light-weight biophysical neuron simulator was developed and optimized to the supercomputer Fugaku. Good strong scaling was demonstrated in a benchmark model upto 152,064 compute nodes with 7.13 petaflops performance. In a more realistic scenario, a whole cerebral cortex of a mouse, consisting of 9 million biophysical neurons and 26 billion synapses, was simulated on the full…
Understanding the brain remains a major scientific challenge due to its complex structure and function. Unlike artificial neural networks, the biological brain features diverse biophysical properties essential to its function. Building an accurate digital replica using extensive anatomical and physiological data, which are available in standardized public databases, has emerged as a promising approach. In this study, a light-weight biophysical neuron simulator was developed and optimized to the supercomputer Fugaku. Good strong scaling was demonstrated in a benchmark model upto 152,064 compute nodes with 7.13 petaflops performance. In a more realistic scenario, a whole cerebral cortex of a mouse, consisting of 9 million biophysical neurons and 26 billion synapses, was simulated on the full-scale Fugaku with 145,728 nodes. These suggest that the present high-performance computing technology is ready to support the construction of a digital replica of the whole mammalian brain.