Work

Modeling macroscopic brain dynamics with brain-inspired computing architecture

Year: 2025

Type: article

Abstract: Understanding the brain requires modeling large-scale neural dynamics, where coarse-grained modeling of macroscopic brain behaviors is a powerful paradigm for linking brain structure to function with empirical data. However, the model inversion process remains computationally intensive and time-consuming, limiting research efficiency and medical deployment. In this work, we present a pipeline bridging coarse-grained brain modeling and advanced computing architectures. We introduce a dynamics-aware quantization framework that enables accurate low-precision simulation with maintained dynamical characteristics, thereby addressing the precision challenges inherent in the brain-inspired computing architecture. Furthermore, to exploit hardware capabilities, we develop hierarchical parallelism mapping strategies tailored for brain-inspired computing chips and GPUs. Experimental results demonstrate that the deployed low-precision models maintain high functional fidelity while achieving tens to hundreds-fold acceleration over commonly used CPUs. This work provides essential computational infrastructures for modeling macroscopic brain dynamics and extends the application of brain-inspired computing to scientific computing in neuroscience and medicine. Modeling macroscopic brain dynamics is essential but limited by the slow and intensive process of fitting models to empirical data. Here, authors introduce a dynamics-aware quantization framework and hierarchical parallelism mapping strategies to enable accurate low-precision simulations on brain-inspired chips and GPUs.

Source: Nature Communications

DOI: 10.1038/s41467-025-64470-3