ARBOR

Arbor is designed to handle very large, very long and computationally intensive problems in neuroscience. Furthermore, Arbor aims to prepare neuroscience users for new HPC architectures. At the same time, Arbor is designed to be easy to use and understand, so that also beginners to computational neuroscience can get up to speed quickly. Whether your model is large or small, Arbor is able to produce optimized code and compute your result on almost any hardware... To achieve abstraction, Arbor makes a distinction between the description of a model, and the execution of a model: a recipe describes a model, and a simulation is an executable instantiation of a model... Arbor draws its strength from the many people that use and improve it. We are happy to consider your contributions (e.g. own models, bug or documentation fixes) for addition to Arbor. We work with the Issue/Pull Request workflow, so head on over to our our code repo to propose your new features or bug fixes.