GSN (Generative Modeling of Signal and Noise)

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GSN is a toolbox for accurately modeling signal and noise distributions in neural datasets. We provide both MATLAB and Python implementations.

GSN is detailed in the following paper:

Kay, K.N., Prince, J.S., Gebhart, T., Tuckute, G., Zhou, J., Naselaris, T., Schutt, H. Disentangling signal and noise in neural responses through generative modeling. bioRxiv (2024).

If you have questions or discussion points, please use the Discussions feature of this GitHub repository. If you find a bug, please let us know by raising a GitHub Issue.

MATLAB

To install:

git clone https://github.com/cvnlab/GSN.git

To use the GSN toolbox, add it to your MATLAB path by running the setup.m script.

Python

To install:

pip install git+https://github.com/cvnlab/GSN.git

Running the example scripts requires:

• installing jupyter notebook or jupyter lab
• installing matplotlib
• cloning the GSN repository in order to get the example scripts located in examples:

pip install jupyterlab
pip install "matplotlib<3.9.0"
git clone https://github.com/cvnlab/GSN.git

Code dependencies: see requirements.txt

Example scripts

We provide a number of example scripts that demonstrate usage of GSN. You can browse these example scripts here:

(Python Example 1 - running GSN on a small dataset of 100 voxels x 200 conditions x 3 trials) https://github.com/cvnlab/GSN/blob/main/examples/example1.ipynb

(MATLAB Example 1 - running GSN on a small dataset of 100 voxels x 200 conditions x 3 trials) https://github.com/cvnlab/GSN/blob/main/matlab/examples/example1.m

If you would like to run these example scripts, the Python versions are available in /GSN/examples, and the MATLAB versions are available in /GSN/matlab/examples.

These notebooks contain a full walkthrough of the process of loading an example dataset, estimating signal and noise distributions using GSN, examining voxel-level noise ceiling estimates, computing the eigenspectrum of both signal and noise covariance matrices, and estimating dimensionality of each.

Additional information

Terms of use: This content is licensed under a BSD 3-Clause License.

If you use GSN in your research, please cite the following paper:

• Kay, K.N., Prince, J.S., Gebhart, T., Tuckute, G., Zhou, J., Naselaris, T., Schutt, H. Disentangling signal and noise in neural responses through generative modeling. bioRxiv (2024).

Change history

• 2024/04/28 - Version 1.0 of GSN released. Accompanies the bioRxiv preprint.

[pre-release updates; early-stage testing]

• 2024/02/25 - Completed port of matlab algorithmic changes to python.
• 2024/01/05 - Major overhaul of GSN matlab functionality by incorporating the biconvex optimization procedure, other minor tweaks.
• 2022/04/13 - Algorithmic changes to covariance estimation added.
• 2022/04/08 - Initial python code version is completed.
• 2022/04/06 - Initial matlab code version is completed.