Larger GPU-accelerated brain simulations with procedural connectivity

Knight, James C and Nowotny, Thomas (2021) Larger GPU-accelerated brain simulations with procedural connectivity. Nature Computational Science. ISSN 2662-8457

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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 optimisation, we can simulate a recent model of the Macaque visual cortex with 4.13 million neurons and 24.2 billion synapses on a single GPU − a significant step forward in making large-scale brain modelling accessible to more researchers.

Item Type: Article
Keywords: GPU computing, Computational Neuroscience
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
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SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 19 Jan 2021 11:28
Last Modified: 04 Feb 2021 11:45

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