Sinha Ankur, Gleeson Padraig, Marin Bóris, Dura-Bernal Salvador, Panagiotou Sotirios, Crook Sharon, Cantarelli Matteo, Cannon Robert C, Davison Andrew P, Gurnani Harsha, Silver Robin Angus
Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom.
Universidade Federal do ABC, São Bernardo do Campo, Brazil.
Elife. 2025 Jan 10;13:RP95135. doi: 10.7554/eLife.95135.
Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open-source software tools for the creation, visualization, validation, and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing, and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.
神经元和神经回路的数据驱动模型对于理解膜电导、突触、树突的特性以及神经元之间的解剖学连接如何在健康和疾病状态下产生脑回路的复杂动态行为至关重要。然而，这些生物过程固有的复杂性使得构建和复用生物细节模型具有挑战性。人们已经开发了各种各样的工具来辅助模型的构建和模拟，但设计和内部表示的差异对那些希望在其研究工作流程中使用数据驱动模型的人来说构成了技术障碍。NeuroML是一种用于计算神经科学的模型描述语言，旨在解决建模工具中的这种碎片化问题。自诞生以来，NeuroML已发展成为一个成熟的社区标准，涵盖了计算神经科学中的广泛模型类型和方法。它推动了一个由可互操作的开源软件工具组成的大型生态系统的发展，用于创建、可视化、验证和模拟数据驱动模型。在此，我们描述了如何将NeuroML生态系统纳入研究工作流程，以简化神经系统标准化模型的构建、测试和分析，并支持FAIR（可查找性、可访问性、互操作性和可复用性）原则，从而促进开放、透明和可重复的科学研究。
