Modeling macroscopic brain dynamics with brain-inspired computing architecture.

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 ma

SUBMITTER: Zheng Z 

PROVIDER: S-EPMC12552719 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature