The BSB is a large-scale modelling framework for Python that allows users to model their brain region as a hierarchical collection of shapes such as layers or meshes and then use a wide assortment of placement and connectivity strategies to populate this model with neurons that have individual positions and cell pair specific connectivity. On top of this cells can also be assigned their own morphologies and rotations. It can deal with all common placement and connectivity paradigms from simple random placement of point neurons, regular placement in grids or arrays to ad-hoc user defined strategies or from simple distance-based connectivity to ultra-precise performant intersection of cell morphologies. It is the first open-source framework to offer such advanced features for the placement and connection of cells, with full integration of cell morphologies in the context of connectivity

It is a bottom-up multiscale modelling which manages simulations in both NEURON and NEST, making it extremely well suited for sophisticated large-scale multicompartmental or point-neuron models. It can parallelize the reconstruction and simulation of arbitrarily large network models, with effortless setup on HPC systems. Model reconstruction and simulation can all be described in JSON, this configuration format serves as a steppingstone into the rich Python library that powers the framework and gives users full freedom to modify and enrich the configured model to their needs.