Skip to content

Latest commit

 

History

History
72 lines (50 loc) · 3.35 KB

README.rst

File metadata and controls

72 lines (50 loc) · 3.35 KB
Raw

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

   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.

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