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NESTML:一种通用建模语言和代码生成工具,用于模拟带有高级可塑性规则的尖端神经网络
Charl Linssen1,2,3, Pooja N Babu1,3, Jochen M Eppler1
1Simulation and Data Lab Neuroscience, Jülich Supercomputer Centre, Institute for Advanced Simulation, Jülich-Aachen Research Alliance, Forschungszentrum Jülich GmbH, Jülich, Germany.
Frontiers in neuroinformatics
|June 19, 2025
概括
NESTML提供了一个特定领域的语言,用于创建计算神经科学模型. 它的代码生成确保模型在各个平台上都是 FAIR (可查找,可访问,可互操作,可重复使用).
科学领域:
- 计算神经科学是一种神经科学.
- 神经科学模型发展模型
- 模拟软件工程 模拟软件工程
背景情况:
- 模型复杂性的增加需要模型的重复使用和演变.
- 确保模拟后端和硬件之间的互操作性是一项挑战.
- 遵守FAIR原则 (可查找,可访问,可互操作,可重复使用) 对科学模型至关重要.
研究的目的:
- 引入NESTML,一种用于描述神经元和突触模型的特定领域语言.
- 解决计算神经科学中符合FAIR模型的需求.
- 促进大规模神经元网络的开发和模拟.
主要方法:
- 开发NESTML,用于神经科学模型的特定领域语言.
- 实现一个代码生成工具链,用于自动生成C++代码 (例如,用于NEST模拟器).
- 扩展工具链以支持各种模拟平台,包括SpiNNaker硬件.
主要成果:
- NESTML允许创建可访问,可重复使用和可互操作的神经元和突触模型.
- 代码生成确保了大规模模拟的高效运行时性能和可扩展性.
- 该工具链支持多个模拟平台,提高了模型的可访问性.
结论:
- NESTML为更广泛的研究社区提供神经元网络模型开发和模拟的便利.
- 语言和代码生成方法促进了计算神经科学中的FAIR原则.
- 由于NESTML的灵活性和可定制性,可以轻松与新的模拟平台集成.
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