https://github.com/adaptivemotorcontrollab/cebra-demos

CEBRA Demo Notebooks. Please see all of them at the URL below:

https://github.com/adaptivemotorcontrollab/cebra-demos

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CEBRA Demo Notebooks. Please see all of them at the URL below:

README.rst

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 
   Encoding of space, hippocampus (CA1) 
   Decoding movie features from (V1) visual cortex 
   Forelimb dynamics, somatosensory (S1) 
   Synthetic neural benchmarking 
   Hypothesis-driven analysis 
   Consistency 
   Decoding 
   Topological data analysis 
   Unified encoders via behavioral alignment 
   Technical: Training models across animals 
   Technical: conv-piVAE 
   Technical: S1 training with MSE loss 
   Technical: Learning the temperature parameter 
   Demo: Using OpenScope Data 
   Demo: Using Dandi Data 
   Demo: Using CEBRA on DeepLabCut outputs 
   Explainability: xCEBRA on RatInABox dataset 
   

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 ` 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:

.. code-block::

   #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  `_.

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