Top Level Guide#


The Brain Scaffold Builder revolves around the Scaffold object. A scaffold ties together all the information in the Configuration with the Storage. The configuration contains your model description, while the storage contains your model data, like concrete cell positions or connections.

Using the scaffold object one can turn the abstract model configuration into a concrete storage object full of neuroscience. For it to do so, the configuration needs to describe which steps to take to place cells, called Placement, which steps to take to connect cells, called Connectivity, and what representations to use during Simulation for those cells and connections. All of these configurable objects can be accessed from the scaffold object, under network.placement, network.connectivity, network.simulations, …

Using the scaffold object, you can inspect the data in the storage by using the PlacementSet and ConnectivitySet APIs. PlacementSets can be obtained with scaffold.get_placement_set, and ConnectivitySets with scaffold.get_connectivity_set.

Ultimately this is the goal of the entire framework: To let you explicitly define every component and parameter that is a part of your model, and all its parameters, in such a way that a single CLI command, bsb compile, can turn your configuration into a reconstructed biophysically detailed large scale neural network.