Department of Computational Biology, Royal Institute of Technology, 114 21 Stockholm, Sweden.
Network. 2012;23(4):254-71. doi: 10.3109/0954898X.2012.737087. Epub 2012 Nov 1.
Large-scale neural simulations encompass challenges in simulator design, data handling and understanding of simulation output. As the computational power of supercomputers and the size of network models increase, these challenges become even more pronounced. Here we introduce the experimental scalable neural simulator Nexa, for parallel simulation of large-scale neural network models at a high level of biological abstraction and for exploration of the simulation methods involved. It includes firing-rate models and capabilities to build networks using machine learning inspired methods for e.g. self-organization of network architecture and for structural plasticity. We show scalability up to the size of the largest machines currently available for a number of model scenarios. We further demonstrate simulator integration with online analysis and real-time visualization as scalable solutions for the data handling challenges.
大规模神经模拟涵盖了模拟器设计、数据处理和模拟输出理解方面的挑战。随着超级计算机的计算能力和网络模型的规模不断增加，这些挑战变得更加明显。在这里，我们介绍了实验可扩展神经模拟器 Nexa，用于在高度生物抽象水平上并行模拟大规模神经网络模型，并探索所涉及的模拟方法。它包括发放率模型和使用机器学习启发的方法构建网络的功能，例如网络架构的自组织和结构可塑性。我们展示了在一些模型场景下，可扩展到当前可用的最大机器规模的可扩展性。我们进一步演示了模拟器与在线分析和实时可视化的集成，作为数据处理挑战的可扩展解决方案。
