Green Brain tools, models, and software available to download.
The Spiking Neural Mark-up Language (SpineML) is a declarative XML based model description language for large scale neural network models.
Learn more on the SpineML website.
GeNN is a GPU enhanced Neuronal Network simulation environment based on NVIDIA CUDA technology.
Learn more on the GeNN website.
Below are instructions for obtaining and running the Corridor Centring Model. There are three experiments included, and there is some set up work to configure each. The model can be run on Linux or OSX.
- Download these files.
- Install Qt 5 and download the simulated environment (beeworld) from the GitHub repository. You’ll also need scipy.
- Run QtCreator, load the .pro file and use the default build options, then build the beeworld. Copy the beeworld2 binary (if on Mac you need the one ”inside” the .app package (right click and select ‘show package contents’ to get it). Then replace the beeworld2 file from the zip you downloaded (it is compiled for Mac, but almost certainly won’t work on your computer).
- Install SpineML_2_BRAHMS and BRAHMS as described here. Note the installation locations (on Mac the installation locations are ”inside” the .app package,right click and select ‘show package contents’)
- The first link contains three directories beginning ‘Paper’ – these are the experiments. The cc_XXXX_model directories are the SpineML models. You now need to configure each experiment for your system – replace the SML_2_B_dir, SML_dir and Model_dir variables in run_FigX.py and analyse_FigX.py with the SpineML_2_BRAHMS, SystemML and model directories on your system, respectively.
python run_FigX.py && python analyse_FigX.py
- You will get a labelled graph of the model output when the batch run is complete.
The central complex model presented here is described in the PLOS One paper: A Computational Model of the Integration of Landmarks and Motion in the Insect Central Complex.
The model along with the data used to create the Figures and the analysis scripts used can be found on the Github website.
A video of the model in action: