/Synchronization-by-Uncorrelated-Noise

Primary LanguageJupyter NotebookMIT LicenseMIT

Synchronization by Uncorrelated Noise

This repository contains the source code of the paper Synchronization through uncorrelated noise in excitatory-inhibitory networks.

Relevant Folders

  • synchronization (python package which contains the complete source code)
  • notebooks (contains jupyter notebooks that utilize the synchronization package)
  • models (target destination for model files created at the end of a run)

Relevant Notebooks

  • notebooks/2_nets_ING.ipynb reproduces scenario 1: two all-to-all coupled interacting inhibitory networks.
  • notebooks/2_nets_PING_all_to_all.ipynb reproduces scenario 2: two all-to-all coupled excitatory-inhibitory networks.
  • notebooks/2_nets_PING_sparse.ipynb reproduces scenario 3: two sparse random excitatory-inhibitory networks.

Development Guide

  • Python 3.6+ is required
  • We recommend to use a virtual environment

Install all required packages with

pip install -r requirements.txt

Install synchronization package locally so that the Jupyter notebooks can import it

pip install -e .

Any change to the code in synchronization/ is immediately reflected as -e installs the package in editable mode.

Jupyter Extensions

We recommend installing the jupyterlab-toc extension as some notebooks are grouped into sections and subsections. By using a TOC extension, reading and editing the notebooks becomes considerably easier.