NeuroML: Reproducibility and Rigour in Computational Neuroscience
Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. This lecture provides an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.
- What is NeuroML
- Why NeuroML is needed
- Tools that support NeuroML
- Role of NeuroML in supporting efficient and collaborative modelling
- Overview of projects providing open source
Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. Here I provide an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.