LaDynS: Latent Dynamic Analysis via Sparse Banded Graphs

This repo consists of

1. Python package ladyns which implements LaDynS [1]
2. Experimental data analyzed in [1]
3. Reproducible IPython notebooks for simulation and experimental data analysis in [1]

Install

Prerequisite

Package ladyns requires:

1. Python >= 3.5
2. numpy >= 1.8
3. matplotlib and scipy.

Git clone

Clone this repo through github:

git clone https://github.com/HeejongBong/ladyns.git

Python install

Install package ladyns using setup.py script:

python setup.py install

Experimental data

The data are available in /data/. The data file consists of lfp_bred_1.mat, lfp_bred_2.mat, and lfp_bred_3.mat which are the beta band-passed filtered LFP in PFC and V4 for 3 thousand trials, respectively. These data are the results of the preprocess by /example/4_0_preprocess_experimental_data.ipynb from the original data collected by Khanna, Scott, and Smith (2020) [2].

Reproducible Ipython notebooks

The scripts are available in /example/. The scripts for the simulation analysis are provided in Python notebook from 3_1_...ipynb to 3_3_...ipynb. The scripts for the experimental data analysis are provided in Python notebook 4_1_analyze_experimental_data.ipynb

References

[1] Bong, H., Yttri, E., Smith, M. A., Ventura, V., & Kass, R. E. (2020). Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings. Submitted to Annals of Applied Statistics.

[2] Khanna, S. B., Scott, J. A., & Smith, M. A. (2020). Dynamic shifts of visual and saccade signals in prefrontal cortical regions 8Ar and FEF. Journal of neurophysiology. 124.6 (2020): 1774-1791.