HNN install (Ubuntu)

Prerequisite: Miniconda

• Run the commands below from a terminal window (as a regular user). This will create a python environment isolated from other installations on the system.

    cd /tmp/
    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
    sh ./Miniconda3-latest-Linux-x86_64.sh -b
    rm /tmp/Miniconda3-latest-Linux-x86_64.sh
  

Prepare the Python environment

1. Create a conda environment with the Python prerequisites for HNN.

    conda env create -f environment.yml
   

2. Activate the HNN conda environment and python prerequisite packages

    conda activate hnn
    pip install https://api.github.com/repos/jonescompneurolab/hnn-core/zipball/master
    pip install nlopt
    pip install mpi4py
   

Download HNN source code

git clone https://github.com/jonescompneurolab/hnn.git
cd hnn

Run the HNN model

1. Start the HNN GUI from a terminal window. Make sure the hnn environment has been activated each time a terminal window is opened:

    conda activate hnn
    python hnn.py
   

2. The HNN GUI should show up. Make sure that you can run simulations by clicking the ‘Run Simulation’ button. This will run a simulation with the default configuration. After it completes, graphs should be displayed in the main window.

3. You can now proceed to running the tutorials at https://hnn.brown.edu/index.php/tutorials/ . Some things to note:

   • A directory called “hnn_out” exists in your home directory where the results from your simulations (data and param files) will be stored.

Upgrading to a new version of HNN

HNN Releases can be found on the GitHub releases page. You can also be notified of new releases by watching the hnn repository on GitHub.

To download the latest HNN release:

curl --remote-name https://github.com/jonescompneurolab/hnn/releases/latest/download/hnn.tar.gz
mkdir hnn_source_code
tar -x --strip-components 1 -f hnn.tar.gz -C hnn_source_code
cd hnn_source_code
conda activate hnn
python3 hnn.py

If you are using git, then run git pull origin master from the source code directory.

Troubleshooting

If you run into other issues with the installation, please open an issue on our GitHub. Our team monitors these issues and will investigate possible fixes.

Another option for users that are running into problems with the above methods, we provide a VirtualBox VM pre-installed with HNN.

• Virtualbox install instructions