GeNN is a GPU enhanced Neuronal Network simulation environment based on NVIDIA CUDA technology.
Welcome to GeNN.
|Read the full online documentation||Download .zip file||Download .tar.gz file|
Download the full documentation as .pdf
|Download .zip file||Download .tar.gz file|
Meet the Team
GeNN is maintained by genn-team.
|Dr James Knight is an independent research fellow, working on developing the potential of SNNs for machine learning at the School of Engineering and Informatics at the University of Sussex|
|Dr James Turner is a post-doctoral researcher, working on the Human Brain Project at the School of Engineering and Informatics at the University of Sussex|
|Prof Thomas Nowotny is a Professor of Informatics at the University of Sussex|
Watch GeNN on GitHub.
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Send an email to the team (James).
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