Estimating Functional and Interventional Connectivity in Neural Data

This code package implements a series of functional connectivity methods including information theory-based and attractor reconstruction-based measures, and introduces methods for computing interventional connectivity based on perturbed neural data. Codes for simulating time series from popular dynamical systems are implemented for exploratory purposes.

See our paper for further details:

@article{PhysRevResearch.5.043211,
  title = {Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics},
  author = {Nejatbakhsh, Amin and Fumarola, Francesco and Esteki, Saleh and Toyoizumi, Taro and Kiani, Roozbeh and Mazzucato, Luca},
  journal = {Phys. Rev. Res.},
  volume = {5},
  issue = {4},
  pages = {043211},
  numpages = {14},
  year = {2023},
  month = {Dec},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevResearch.5.043211},
  url = {https://link.aps.org/doi/10.1103/PhysRevResearch.5.043211}
}

Note: This research code remains a work-in-progress to some extent. It could use more documentation and examples. Please use at your own risk and reach out to us (anejatbakhsh@flatironinstitute.org) if you have questions. If you are using this code package, please cite our paper.

A short and preliminary guide

Installation Instructions

1. Download and install anaconda
2. Create a virtual environment using anaconda and activate it

conda create -n fcf python=3.8
conda activate fcf

3. Install fcf package

git clone https://github.com/amin-nejat/fcf.git
cd fcf
pip install -r requirements.txt 
pip install -e .

4. Run demo file

python demo.py

Since the code is preliminary, you will be able to use git pull to get updates as we release them.