Awile Omar, Kumbhar Pramod, Cornu Nicolas, Dura-Bernal Salvador, King James Gonzalo, Lupton Olli, Magkanaris Ioannis, McDougal Robert A, Newton Adam J H, Pereira Fernando, Săvulescu Alexandru, Carnevale Nicholas T, Lytton William W, Hines Michael L, Schürmann Felix
Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States.
Front Neuroinform. 2022 Jun 27;16:884046. doi: 10.3389/fninf.2022.884046. eCollection 2022.
The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.
对可重复、可信的多尺度生物建模的需求推动了标准化模拟平台的发展，例如广泛用于计算神经科学的NEURON环境。几十年来开发和维护NEURON需要关注向后兼容性、不断发展的计算机架构、新尺度和物理过程的添加、新用户的可及性以及专家的效率和灵活性等相互竞争的需求。为了应对这些挑战，我们现在已对NEURON进行了实质性的现代化改进，提供持续集成、改进的构建系统和发布工作流程，以及更好的文档。借助NMODL领域特定语言的新源到源编译器，我们通过CoreNEURON模拟引擎增强了NEURON在包括GPU在内的各种硬件平台上高效运行的能力。通过实施优化的内存中传输机制，用户可以轻松访问这个性能优化的后端，提供从笔记本电脑到工作站再到超级计算机和云平台的培训和模型开发路径。同样，我们通过使用即时编译加速了NEURON的反应扩散模拟性能。我们表明，这些努力带来了不断增长的开发者群体、更简单且更强大的软件发行、更广泛支持的计算机架构、NEURON与其他科学工作流程的更好集成，以及生物物理和生化模型模拟性能的大幅提升。
