CuBNM: GPU-Accelerated Brain Network Modeling.

ABSTRACT: Brain network modeling uses computer simulations to infer about latent neural properties at micro- and mesoscales by fitting brain dynamic models to empirical data of individual subjects or groups. However, computational costs of (individualized) model fitting is a major bottleneck, limiting the practical feasibility of this approach to larger cohorts and more complex models, and highlighting the need for scalable simulation implementations. Here, we introduce cuBNM, a Python package which leverages parallel processing of graphics processing units to massively accelerate simulations of brain network models. We show running simulations on graphics processing units is several hundred times faster compared to central processing units. We demonstrate the usage of cuBNM by running optimi

SUBMITTER: Saberi A 

PROVIDER: S-EPMC12642494 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature