# How Brian2GeNN works inside¶

The Brian2GeNN interface is providing middleware to use the GeNN simulator framework as a backend to the Brian 2 simulator. It has been designed in a way that makes maximal use of the existing Brian 2 code base by deriving large parts of the generated code from the cpp_standalone device of Brian 2.

## Model and user code in GeNN¶

In GeNN a simulation is assembled from two main sources of code. Users of GeNN provide “code snippets” as C++ strings that define neuron and synapse models. These are then assembled into neuronal networks in a model definition function. Based on the mdoel definition, GeNN generates GPU and equivalent CPU simulation code for the described network. This is the first source of code.

The actual simulation and handling input and output data is the responsibility of the user in GeNN. Users provide their own C/C++ code for this that utilizes the generated code described above for the core simulation but is otherwise fully independent of the core GeNN system.

In the Brian2GeNN both the model definition and the user code for the main simulation are derived from the Brian 2 model description. The user side code for data handling etc derives more or less directly from the Brian 2 cpp_standalone device in the form of GennUserCodeObjects. The model definition code and “code snippets” derive from separate templates and are capsulated into GeNNCodeObjects.

## Code generation pipeline in Brian2GeNN¶

The model generation pipeline in Brian2GeNN involves a number of steps. First, Brian 2 performs the usual interpretation of equations and unit checking, as well as, applying an integration scheme onto ODEs. The resulting abstract code is then translated into C++ code for GeNNUserCodeObjects and C++-like code for GeNNCodeObjects. These are then assembled using templating in Jinja2 into C++ code and GeNN model definition code. The details of making Brian 2’s cpp_standalone code suitable for the GeNN user code and GeNN model definition code and code snippets are taken care of in the GeNNDevice.build function.

Once all the sources have been generated, the resulting GeNN project is built with the GeNN code generation pipeline. See the GeNN manual for more details on this process.

## Templates in Brian2GeNN¶

The templates used for code generation in Brian2GeNN, as mentioned above, partially derive from the cpp_standalone templates of Brian 2. More than half of the templates are identical. Other templates, however, in particular for the model definition file and the main simulation engine and main entry file “runner.cc” have been specifically written for Brian2GeNN to produce a valid GeNN project.

## Data transfers and results¶

In Brian 2, data structures for initial values and synaptic connectivities etc are written to disk into binary files if a standalone device is used. The executable of the standalone device then reads the data from disk and initializes its variables with it. In Brian2GeNN the same mechanism is used, and after the data has been read from disk with the native cpp_standalone methods, there is a translation step, where Brian2GeNN provides code that translates the data from cpp_standalone arrays into the appropriate GeNN data structures. The methods for this process are provided in the static (not code-generated) “b2glib”.

At the end of a simulation, the inverse process takes place and GeNN data is transfered back into cpp_standalone arrays. Native Brian 2 cpp_standalone code is then invoked to write data back to disk.

If monitors are used, the translation occurs at every instance when monitors are updated.

## Memory usage¶

Related to the implementation of data flows in Brian2GeNN described above the host memory used in a run in brian2GeNN is about twice what would have been used in a Brian 2 native cpp_standalone implementation because all data is held in two different formats - as cpp_standalone arrays and as GeNN data structures.