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Larger GPU-accelerated brain simulations with procedural connectivity

Abstract

Simulations are an important tool for investigating brain function but large models are needed to faithfully reproduce the statistics and dynamics of brain activity. Simulating large spiking neural network models has, until now, needed so much memory for storing synaptic connections that it required high performance computer systems. Here, we present an alternative simulation method we call ‘procedural connectivity’ where connectivity and synaptic weights are generated ‘on the fly’ instead of stored and retrieved from memory. This method is particularly well suited for use on graphical processing units (GPUs)—which are a common fixture in many workstations. Using procedural connectivity and an additional GPU code generation optimization, we can simulate a recent model of the macaque visual cortex with 4.13 × 106 neurons and 24.2 × 109 synapses on a single GPU—a significant step forward in making large-scale brain modeling accessible to more researchers.

A preprint version of the article is available at bioRxiv.

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Fig. 1: Scaling of 1 s balanced random network simulation using different algorithms on a range of modern GPUs.
Fig. 2: Performance of a 1 s simulation of 1 × 106 LIF neurons driven by a Gaussian input current, partitioned into varying numbers (Npop) of populations and running on a workstation equipped with a Titan RTX GPU.
Fig. 3: Results of full-scale multi-area model simulation.
Fig. 4: Comparison of full-scale multi-area model spike statistics between three GeNN simulations with different seeds; and between the three GeNN simulations and a NEST simulation.

Data availability

The raw data40 used to produce Figs. 1 and 2; and the pre-processed data used to produce Fig. 3 are available at https://doi.org/10.5281/zenodo.4277749. Furthermore, the raw data used to produce Figs. 1 and 2 are also available with this manuscript. The raw spiking data from the GeNN simulations of the multi-area model41 are available at https://doi.org/10.25377/sussex.12912699. The authors of ref. 15 hold the copyright for the raw spiking data from their NEST simulations and the data is available from them upon request.

Code availability

All experiments were carried out using GeNN 4.3.3 (ref. 42), available at https://doi.org/10.5281/zenodo.4022384. A GeNN port of the multi-area model43 is available at https://doi.org/10.5281/zenodo.4271816. The models used to produce Figs. 1 and 2 as well as the code to produce all figures are available at https://doi.org/10.5281/zenodo.4277749. The latest version of the code used for this paper is also availalble at https://github.com/BrainsOnBoard/procedural_paper/.

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Acknowledgements

We would like to thank J. Pronold, S. van Albada, A. Korcsak-Gorzo and M. Schmidt for their help with the multi-area model data; and D. Goodman and M. Mikaitis for their feedback on the manuscript. J.K. and T.N. were funded by the EPSRC (Brains on Board project, grant number EP/P006094/1). T.N. was also funded by the the European Union’s Horizon 2020 research and innovation program under grant agreements 785907 (HBP SGA2) and 945539 (HBP SGA3).

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J.K. and T.N. wrote the paper. T.N. is the original developer of GeNN. J.K. is currently the primary GeNN developer and was responsible for extending the code generation approach to the procedural simulation of synaptic connectivity. J.K. performed the experiments and the analysis of the results that are presented in this work.

Corresponding author

Correspondence to James C. Knight.

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The authors declare no competing interests.

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Peer review informationNature Computational Science thanks Susanne Kunkel, Oliver Rhodes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Yann Sweeney was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Knight, J.C., Nowotny, T. Larger GPU-accelerated brain simulations with procedural connectivity. Nat Comput Sci 1, 136–142 (2021). https://doi.org/10.1038/s43588-020-00022-7

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