GeNN by genn-team
GeNN is a GPU enhanced Neuronal Network simulation environment based on NVIDIA CUDA technology.
Welcome to GeNN.
GeNN 4.9.0 | ||
| Read the full online documentation | Download .zip file | Download .tar.gz file |
GeNN 3.3.0 | ||
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Read
the full
online documentation Download the full documentation as .pdf |
Download .zip file | Download .tar.gz file |
Meet the Team
GeNN is maintained by genn-team.
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Dr James Knight is an EPSRC research software engineering fellow, working on developing the potential of SNNs for machine learning at the School of Engineering and Informatics at the University of Sussex |
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Prof Thomas Nowotny is a Professor of Informatics at the University of Sussex |
Get involved
Watch GeNN on GitHub.
Ask a question.
Submit an issue on GitHub.
Send an email to the team (James).
Follow the Twitter account of the Brains on Board Project, which is partially supporting the development of GeNN (financed by the EPSRC)
Publications
Knight, J. C., Komissarov, A., & Nowotny, T. (2021). PyGeNN: A Python Library for GPU-Enhanced Neural Networks. Frontiers in Neuroinformatics, 15(April), 1–12. Access online
Knight, J. C., & Nowotny, T. (2021). Larger GPU-accelerated brain simulations with procedural connectivity. Nature Computational Science, 1, 136-142. Access online
Knight, J. C., & Nowotny, T. (2018). GPUs Outperform Current HPC and Neuromorphic Solutions in Terms of Speed and Energy When Simulating a Highly-Connected Cortical Model. Frontiers in Neuroscience, 12(December), 1–19. Access online
Yavuz, E., Turner, J. and Nowotny, T. (2016) GeNN: a code generation framework for accelerated brain simulations. Scientific Reports 6, 18854. Access online