Learning objectives

In this tutorial, you will learn how to install PyNN, together with the  Brian 2, NEST, and NEURON simulators, in EBRAINS Jupyter Lab.

Audience

This tutorial is intended for people with at least a basic knowledge of neuroscience (high school level or above) and basic familiarity with the Python programming language. It should also be helpful for people who already have advanced knowledge of neuroscience and neural simulation, who simply wish to learn how to use PyNN, and how it differs from other simulation tools they know.

Prerequisites

To follow this tutorial, you will need an EBRAINS account. You should know how to create and use Jupyter notebooks in the EBRAINS Jupyter Lab.

Format

These tutorials will be screencasts, in which the presenter runs commands in a Jupyer notebook, and the viewer is expected to follow along. The intended duration is 10-15 minutes.

Script

Hello, my name is X.

This video is one of a series of tutorials for PyNN, which is Python software for modelling and simulating spiking neural networks.

For a list of the other tutorials in this series, you can visit ebrains.eu/service/pynn, that's p-y-n-n.

In this tutorial, I will guide you through setting up PyNN, together with the Brian 2, NEST, and NEURON simulators, in the EBRAINS Collaboratory's JupyterLab. Note that we have a dedicated version of this tutorial for other environments, such as Linux, Mac OS, and Windows.

To follow this tutorial, you will need an EBRAINS account. Interested users with a current affiliation to an academic institution can directly create an account at https://ebrains.eu/register/. Users without such an affiliation may submit a request for an account.

EBRAINS JupyterLab only supports Python3, with the current default version being Python 3.6.9. This tutorial presents the steps for such an environment, but changes required to the steps below following upgrades to more recent versions of Python are expected to be minimal.

In this tutorial, we will make use of virtual environments. This allows multiple Python projects to coexist on the same computer, even when they might have different, and even conflicting, requirements. It helps isolate projects, thereby preventing unrequested changes in others, when any one of them is updated.

We begin by creating a directory for our project.

Next, we will create a virtual environment within this directory. Python 3 provides support for creating virtual environments. Since Python 3.6, the recommended method for creating a new virtual environment is as follows:

This will create a sub-directory named 'pynn_env' within our project directory, with several files and sub-directories. Let's take a look at the 'site-packages' directory. 

As you see here, only a limited number of basic packages have currently been installed in this virtual environment. In the steps ahead, we will install various other packages, which you will be able to see here.

To enter this virtual environment, and thereby use its resources in isolation from other projects on your computer, we require to "activate" it. This is achieved by running the command:

Notice how this changes the command prompt to show the name of your virtual environment. In our case, we named it 'pynn_env', and this is now reflected as a prefix to the command prompt. This confirms that we are now in our new virtual environment.

Now that we have our project's virtual environment setup, we are ready to install PyNN and other simulators. In general, it is advisable to install the various simulators (especially NEURON and NEST) prior to installing PyNN, because PyNN will then auto compile NEURON's NMODL fles and NEST's extensions during installation. In this tutorial, we will adopt this approach and begin by installing the simulators. For the purposes of this tutorial, we will demonstrate the installation of Brian2, NEURON, and NEST simulators.

We start here with the installation of Brian 2. Brian 2 can be installed simply by using the pip command.

This will install Brian 2, along with all its dependencies such as 'cython', 'numpy', and so on. We can now go back to our virtual environment's 'site-packages' directory to see how it is populated with all these packages.

To confirm that we have properly installed Brian 2 on our computer, we can test as follows:

If there are no error messages here, and the import is successful, we have completed installing Brian 2.

We will now move on to install the NEST simulator. Unlike Brian 2, NEST is not a Python package and therefore, it cannot be installed via the 'pip' command.

At the time of creating this tutorial, the lastest version of NEST is v3.1. This is currently supported by PyNN v0.10, and it is likely that other versions of NEST are potentially incompatible with this version of PyNN. The installation is done by first adding the PPA repository for NEST and updating apt, followed by the installation of NEST itself.

This installs the NEST module along with PyNEST, which is a Python interface for controlling the NEST kernel. This allows us to use NEST via Python. To confirm that we have properly installed NEST on our computer, we can test as follows:

This will display the NEST banner, which mentions the version amongst other info. Here, as we can see, we have now installed NEST v3.1 on our system. Next, let's next verify that this is indeed accessible via Python.

If there are no error messages here, and the import is successful, we have completed installing NEST simulator and are able to load it via Python.

We next move on to the third simulator, NEURON. The installation for NEURON used to be more complex but can now be easily completed by using the 'pip' command:

This installs the NEURON simulator on your system. To confirm that we have properly installed NEURON, we can test as follows:

This will display the NEURON banner, which mentions the version amongst other info. Here, as we can see, we have now installed NEURON v8.0.0 on our system. Next, let's verify that this is indeed accessible via Python.

If there are no error messages here, and the import is successful, we have completed installing NEURON simulator, and are able to load it via Python.

Now that we have installed all the simulators we intend to use, we move on to installing PyNN itself. Because PyNN is a Python package, we can install it easily by using the 'pip' command:

To verify that PyNN has been successfully installed on our system, and that it is indeed able to communicate with the other simulators that we installed earlier, we can try running:

This confirms that PyNN has been properly setup, and also that is able to employ Brian 2. To verify that PyNN is also able to communicate with NEST and NEURON simulators, we can do the following:

This confirms that all the required software packages have been successfully completed and are functioning as expected.

That is the end of this tutorial, in which I've demonstrated how to install PyNN, and other required simulators, in a Linux system. You are now ready to start modeling! To learn about model development in PyNN, take a look at our next tutorial. Also, we will be releasing a series of tutorials, throughout the rest of 2021 and 2022, to introduce more advanced features of PyNN, so keep an eye on the EBRAINS website.

We have listed here some links that might be of interest to users who wish to find more details about the various softwares employed in this tutorial.

PyNN has been developed by many different people, with financial support from several organisations. I'd like to mention in particular the CNRS and the European Commission, through the FACETS, BrainScaleS, and Human Brain Project grants.

For more information, visit neuralensemble.org/PyNN. If you have questions you can contact us through the PyNN Github project, the NeuralEnsemble forum, EBRAINS support, or the EBRAINS Community.