NESTML [1, 2] was developed to address the maintainability issues that follow from an increasing number of models, model variants, and an increased model complexity in computational neuroscience. Our aim is to ease the modelling process for neuroscientists both with and without prior training in computer science. This is achieved without compromising on performance by automatic source-code generation, allowing the same model file to target different hardware or software platforms by changing a single command-line parameter. While originally developed in the context of the NEST Simulator [3], the language itself as well as the associated toolchain are lightweight, modular and extensible, by virtue of using a parser generator and internal abstract syntax tree (AST) representation, which can be operated on using well-known patterns such as visitors and rewriting.

A typical workflow consists of the following steps: Initially, a model of interest is identified. This model might describe the dynamical behaviour of a single neuron, or the plasticity rules concerning a synapse. The model description is typically in mathematical or textual form, and needs to be converted by the neuroscientist into a format following the NESTML syntax. It is then processed by invoking the toolchain, which generates optimised code for the target platform (e.g. NEST running on a high-performance computing cluster). That code is then dynamically loaded or compiled as part of the simulation framework (in this case, NEST). The model is now ready for use in the simulator, and can be instantiated within a simulation script, written e.g. using the PyNEST API [4], before starting the simulation and performing subsequent analysis.

NESTML is open sourced under the terms of the GNU General Public License v2.0 and is publicly available at https://github.com/nest/nestml. Extensive documentation and automated testing are in place, both for the language itself as well as the associated processing toolchain. Active user support is provided via the GitHub issue tracker and the NEST user mailing list.

References

1. D. Plotnikov et al. (2016) Modellierung March 2-4 2016, Karlsruhe, Germany. 93–108. doi:10.5281/zenodo.1412345
2. K. Perun et al. (2018). Version 2.4, Zenodo. doi:10.5281/zenodo.1319653
3. M.-O. Gewaltig & M. Diesmann (2007) Scholarpedia 2(4), 1430. doi:10.4249/scholarpedia.1430
4. Y.V. Zaytsev & A. Morrison (2014) Front. Neuroinform. 8:23. doi:10.3389/fninf.2014.00023