Welcome

This shared workspace (or "collab") is for participants in the HBP Young Researchers Event 2022 in Copenhagen who are taking part in the hands-on session "Using the PyNN library to build and simulate models on EBRAINS".

In this hands-on session we will work together through a Jupyter notebook which demonstrates the basics of working with PyNN.

Since it is hands-on, each participant should work on their own laptop with their own copy of the notebook. If you'd prefer to work in small groups of 2 or 3 round a single laptop, that's also fine.

This first page contains instructions for getting started with the hands-on session, and a list of resources for getting help and finding further documentation and tutorials.

Resources

• Rocket chat channel for asking questions during or after the session
• PyNN documentation
• PyNN tutorials on YouTube
  • Installing PyNN on a Linux system
  • Building and simulating a simple model using PyNN
• Collaboratory documentation

Instructions

1. So you can save any changes you might make to the notebook, it is best to work in your own collab workspace. If you already have a collab you want to use, open it in a new tab. If not, create a new one by clicking here or clicking on "Collabs" in the top menu then "Create a collab".
2. Copy the Jupyter notebook to your own collab
   1. (option A) click here, select "yes" in the "Grant Access to Collab File Cloner" dialog, select the target collab in the drop-down list, then click "Continue" followed by "Clone File".
   2. (option B) in this collab, click on "Drive". Next to "PyNN_tutorial.ipynb" click on the "more options" button, then "Copy". In "Other Libraries" select your own collab then click "Submit".
3. In your own collab, click on Lab, then select either "Fenix CH" or "Fenix DE".
4. On the "Server Options" page, select "Official EBRAINS Docker image 22.03 for Collaboratory.Lab" then click "Start".
5. In the left-hand file chooser, double-click on "PyNN_tutorial.ipynb".
6. We will work through the notebook together, discussing each cell as we execute it. If you're not familiar with Jupyter notebooks, note that you can run a cell either by clicking the "play" icon or by typing "<shift>-<return>"