Python implementation of Gaussian process current source density (CSD) estimation.
Paper: Klein, N., Siegle, J.H., Teichert, T., Kass, R.E. (2021) Cross-population coupling of neural activity based on Gaussian process current source densities. (preprint: https://arxiv.org/abs/2104.10070)
This code estimates current source density over space and time from local field potential (LFP) recordings.
Full source code available at https://github.com/natalieklein/gpcsd.
Install using pip install gpcsd.
To get the scripts to reproduce the paper results, get the source code from github: https://github.com/natalieklein/gpcsd.
Dependencies should be automatically installed by pip, but also see the Anaconda environment.yml file.
It will install two github-based dependencies with pip that are not able to be included in setup.py.
Directory src/gpcsd contains the main source code. There are classes gpcsd1d.py and gpcsd2d.py for the GPCSD models, in addition to some support functions in other files.
Simulation studies are found in simulation_studies and reproduce all simulation results shown in the paper. See simulation_studies/README.md for a full description of the scripts.
Here we apply GPCSD1D to two-probe auditory cortex LFPs measured in a macaque monkey. The scripts reproduce all results shown in the paper. See auditory_lfp/README.md for more information on the scripts.
The auditory LFP data can be downloaded from https://doi.org/10.5281/zenodo.5137888, or using script download_data.sh.
The code assumes that it will be downloaded into auditory_lfp/data/.
We apply GPCSD2D to LFP recordings from Neuropixels probes in a mouse. This reproduces the figures shown in the paper. See neuropixels/README.md for more detail.
The Neuropixels data can be downloaded from https://doi.org/10.5281/zenodo.5150708, or using script download_data.sh.
The code assumes that it will be downloaded into neuropixels/data/.
