This module provides a quantitative characterization of network states in neocortex from extracellular signals. It implements the analysis described in the following article (please cite if you use this code !):

Network States Classification based on Local Field Potential Recordings in the Awake Mouse Neocortex Yann Zerlaut, Stefano Zucca, Tommaso Fellin, Stefano Panzeri bioRxiv 2022.02.08.479568; doi: https://doi.org/10.1101/2022.02.08.479568

1. Install a python distribution for scientific analysis:

   get the latest Miniconda distribution and install it on your home folder.

2. Run the following in the Anaconda prompt:

git clone https://github.com/yzerlaut/Network_State_Index.gitb
cd Network_State_Index
pip install .

If you do not wish to clone the repository you can also directly:

pip install git+https://github.com/yzerlaut/Network_State_Index

• Run the software GUI

python -m NSI

• Using the notebook implmentation

jupyter notebook notebook_demo.ipynb

• Axon Instruments (pClamp) ".abf" format

• HDF5 ".h5" format

• Numpy storing formats (".npz" storing a dictionary)

You can set the desired channel to analyze and the gain that should be applied (only if you want it in uV)

It computes the NSI measure over the whole data. It can be a bit long if the data are large.

In the top 3 plots, we show the full (subsampled) data.

In the bottom 3 plots, we show a zoomed (subsampled) portion of the data. Highlighted with a red filled rectangle in the top plot.

• Zoom1: When clicking on this button, you can select a time window in the top plot
• Zoom2: When clicking on this button, you can select a time window in the bottom-Vext plot

The output is stored as an hdf5 datafile. It containes the sample times of validated network states and their associated NSI level.