Creating NeuroML models

There are 3 main ways of developing a new model of a neuronal system in NeuroML

1) Reuse elements from previous NeuroML models

There are an increasing number of resources where you can find and analyse previously developed NeuroML models to use as the basis for a new model. See here for details.

2) Writing models from scratch using Python NeuroML tools

The toolchain around NeuroML means that it is possible to create a model in NeuroML format from the start. Please see the Getting Started with NeuroML section for quick examples on how you can use pyNeuroML to create NeuroML models and run them.

3) Convert a published model developed in a simulator specific format to NeuroML

Most computational models used in publications are released in the particular format used by the authors during their research, often in a general purpose simulator like NEURON. Many of these can be found on ModelDB. Converting one of these to NeuroML format will mean that all further developments/modifications of the model will be standards compliant, and will give access to all of the NeuroML compliant tools for visualising/analysing/optimising/sharing the model, as well as providing multiple options for executing the model across multiple simulators.

The next page is a step by step guide to creating a new NeuroML model based on an existing published model, verifying its behaviour, and sharing it with the community on the Open Source Brain platform.