Demo Notebooks

We provide a set of demo notebooks to get started with using CEBRA. To run the notebooks, you need a working Jupyter notebook server, a CEBRA installation, and the datasets required to run the notebooks, available on FigShare.

.. nbgallery::
   :maxdepth: 2

   Getting Started with CEBRA <demo_notebooks/CEBRA_best_practices.ipynb>
   Encoding of space, hippocampus (CA1) <demo_notebooks/Demo_hippocampus.ipynb>
   Decoding movie features from (V1) visual cortex <demo_notebooks/Demo_Allen.ipynb>
   Forelimb dynamics, somatosensory (S1) <demo_notebooks/Demo_primate_reaching.ipynb>
   Synthetic neural benchmarking <demo_notebooks/Demo_synthetic_exp.ipynb>
   Hypothesis-driven analysis <demo_notebooks/Demo_hypothesis_testing.ipynb>
   Consistency <demo_notebooks/Demo_consistency.ipynb>
   Decoding <demo_notebooks/Demo_decoding.ipynb>
   Topological data analysis <demo_notebooks/Demo_cohomology.ipynb>
   Technical: Training models across animals <demo_notebooks/Demo_hippocampus_multisession.ipynb>
   Technical: conv-piVAE <demo_notebooks/Demo_conv-pivae.ipynb>
   Technical: S1 training with MSE loss <demo_notebooks/Demo_primate_reaching_mse_loss.ipynb>
   Technical: Learning the temperature parameter <demo_notebooks/Demo_learnable_temperature.ipynb>
   Demo: Using OpenScope Data <demo_notebooks/Demo_openscope_databook.ipynb>
   Demo: Using Dandi Data <demo_notebooks/Demo_dandi_NeuroDataReHack_2023.ipynb>

The demo notebooks can also be found on GitHub.

Installation

Before you can run these notebooks, you must have a working installation of CEBRA. Please see the dedicated :doc:`Installation Guide </installation>` for information on installation options using conda, pip and docker.

Synthetic Experiment Demo (CEBRA, piVAE, tSNE, UMAP): This demo requires several additional packages that have differing requirements to CEBRA. Therefore, we recommend using the supplied docker container or conda cebra-full env.

Demo Data

We host prepackaged data on figshare. And several of the demo notebooks have an automatic data download function.

If you don't see the auto-download, and you use Google Colaboratory, you can easily add the following code into an early cell in the notebook to directly download and use:

#for google colab only, run this cell to download and extract data:
!wget --content-disposition https://figshare.com/ndownloader/files/36869049?private_link=60adb075234c2cc51fa3
!mkdir data
!tar -xvf "/content/data.tgz" -C "/content/data"

For different paths, you can specify the CEBRA_DATADIR=... environment variable. You can do this by placing import os; os.environ['CEBRA_DATADIR'] = "path/to/your/data" at the top of your notebook.

Contributing

We welcome Demo notebooks from others! Please fork the repo, add your notebook, check that it works on Google Colaboratory (remove the launch button within your PR), and then open a PR! Please also edit the "gallery" list (see the soure code for this page), and finally, add an icon here, then a path to the icon here.

For reference, the original open-source data we used in Schneider, Lee, Mathis 2023 is available at:

• Hippocampus dataset, using a preprocessing script.
• Primate S1 dataset.
• Allen Institute Neuropixels dataset and 2P dataset.