
NeuroML은 신경과학 분야의 데이터 기반 모델링을 단순화하고 상호운용성을 높여 개방적이고 투명하며 재현 가능한 과학을 촉진한다.


coremsg

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 make 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) data principles, thus promoting open, transparent and reproducible science.

### Competing Interest Statement

The authors have declared no competing interest.

The NeuroML ecosystem for standardized multi-scale modeling in neuroscience

신경과학-분야의-표준화된-다중-규모-모델링을-위한-neuroml-생태계


신경과학 분야의 표준화된 다중 규모 모델링을 위한 NeuroML 생태계
