JuRSE Code of the Month - June 2026

Each month we highlight a code from Forschungszentrum Jülich and this month's code is cuBNM, developed at the Institute of Neurosciences and Medicine, Brain and Behaviour (INM-7).

cuBNM
The cuBNM toolbox uses GPUs to efficiently run simulations of brain network models consisting of nodes which are connected through a connectome and fit them to empirical neuroimaging data through integrated optimization algorithms. Several commonly used models (e.g., reduced Wong-Wang, Jansen-Rit, Kuramoto, Wilson-Cowan) are implemented, and new models can be added via YAML definition files.

JuRSE selected highlights
When looking at the repository and its contributors, you might think "Huh? PhDware as Code of the Month?" Yes! Why? Because the (sole) author of this research software spent lots of thoughts, time, and energy on writing a proper documentation with tutorials, on an accessible RSD entry, on a clean and polished repository, and even an accompanying paper. Sure, having just one contributor is not a sustainable way to maintain research software, but not all projects have to run forever. And who knows, with all this effort going into a great presentation of the code, someone might pick it up. cuBNM is ready for this and the author demonstrates that "PhDware = crappy RSE techniques" does not have to be true.

More information
Website: https://cubnm.readthedocs.io
RSD: https://helmholtz.software/software/cubnm
GitHub: https://github.com/amnsbr/cubnm