Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.
Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.
复制标题
在HPC基础设施上部署和优化大规模尖峰神经网络的具体化模拟。
DOI：
10.3389/fninf.2022.884180
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发表时间：
2022
影响因子：
3.5
通讯作者：
Morin, Fabrice O.
中科院分区：
文献类型：
--
作者：
Feldotto, Benedikt;Eppler, Jochen Martin;Jimenez-Romero, Cristian;Bignamini, Christopher;Gutierrez, Carlos Enrique;Albanese, Ugo;Retamino, Eloy;Vorobev, Viktor;Zolfaghari, Vahid;Upton, Alex;Sun, Zhe;Yamaura, Hiroshi;Heidarinejad, Morteza;Klijn, Wouter;Morrison, Abigail;Cruz, Felipe;McMurtrie, Colin;Knoll, Alois C.;Igarashi, Jun;Yamazaki, Tadashi;Doya, Kenji;Morin, Fabrice O.
关键词：
在闭环中模拟大脑-身体-环境三位一体是一个有吸引力的建议，可以研究感知、运动活动以及与环境的相互作用如何塑造大脑活动，反之亦然。然而，这种具体方法的相关性完全取决于各种模拟现象的建模复杂性。在本文中，我们介绍了一个软件框架，该框架能够模拟生物力学精确的肌肉骨骼系统中包含的尖峰神经元的大规模、生物真实网络，该系统与物理真实的虚拟环境交互。我们将该框架部署在 EBRAINS 研究基础设施的高性能计算资源上，并通过在越来越多的互连计算节点上分配计算来研究扩展性能。我们的架构基于请求的计算节点以及持久虚拟机；这提供了一个无需专业知识的多领域用户即可访问的高性能仿真环境，使用户能够通过基于网络的图形用户界面以自定义规模实例化和控制仿真。我们的模拟环境完全开源，基于在人脑项目和 NEST 模拟器的背景下开发的神经机器人平台。我们通过两个基准实验，通过研究扩展计算资源对实验运行时间、大脑实例化和模拟时间定义的性能的影响，来表征大规模实体大脑模拟的并行架构的能力。第一个基准是基于大规模平衡网络，而第二个基准是由超过一百万个神经元和十亿个突触组成的多区域具体大脑模拟。这两个基准测试都清楚地显示了扩展计算资源如何以近线性的方式提高上述性能指标。第二个基准特别表明了高度分布式模拟在计算速度和资源成本之间的权衡方面的潜力和局限性。我们的模拟架构正在准备作为 EBRAINS 服务供每个人使用，从而提供具有独特工作流程的社区范围的工具，为计算神经科学社区内闭环实施的研究提供动力。
Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.
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DOI：
10.1111/ejn.14869
发表时间：
2021-04
期刊：
The European journal of neuroscience
影响因子：
--
作者：
Girard B;Lienard J;Gutierrez CE;Delord B;Doya K
通讯作者：
Doya K
DOI：
10.1007/978-1-59745-520-6_7
发表时间：
2007-01-01
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
Bower, James M;Beeman, David
通讯作者：
Beeman, David
影响因子：
20.6
作者：
Furber, Steve B.;Galluppi, Francesco;Plana, Luis A.
通讯作者：
Plana, Luis A.
影响因子：
3
作者：
Djurfeldt, Mikael;Hjorth, Johannes;Ekeberg, Orjan
通讯作者：
Ekeberg, Orjan
影响因子：
2.5
作者：
Azevedo, Frederico A. C.;Carvalho, Ludmila R. B.;Herculano-Houzel, Suzana
通讯作者：
Herculano-Houzel, Suzana