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The Virtual Brain

"The Virtual Brain" Project (TVB Project) has the purpose of offering modern tools to the Neurosciences community, for computing, simulating and analyzing functional and structural data of human brains, brains modeled at the level of population of neurons.

  1. To install TVB code on your machine, we recommend you to first create a dedicated Python env, and afterwards to take our Pypi released packages:

     pip install tvb-library
     pip install tvb-framework
  2. Alternatively and easier, you could simply download TVB_Distribution from: In this variant, you will have Python, TVB and all our 3rd party dependencies downloaded together, you do not need to do anything else other than unzip and double click on tvb_start command.

  3. This repository holds the python sources for TVB main codebase. If you clone this repository for extending its functionality, first create a dedicated Python env, select it and then run inside tvb_build folder the following command to link tvb-sources as libraries:



To contribute to this repo, you are advised to first fork it under GitHub (through the web ui), and next clone that fork on your machine, and use it as you use any other git repository.

More details on our documentation site:

TVB Scientific library (tvb-library)

"TVB Scientific Library" is the most important scientific contribution of TVB Project, but only a part of our code.

"TVB Scientific Library" is a light-weight, stand-alone Python library that contains all the needed packages in order to run simulations and analysis on data without the need for the entire TVB Framework. This implies that no storage will be provided so data from each session will be lost on close. You need to either persist it yourself in some manner or use the full TVBFramework where HDF5 / database storage is provided as default.

TVB Framework (tvb-framework)

The Virtual Brain framework is a complete framework, wrapped over tvb-library, and offering extra features:

  • a plug-able workflow and operations manager;
  • a data persistence layer (with a relational DB and H5 File Storage);
  • an HTML5 based user interface over CherryPy server;
  • visualizers for neuro-science related entities.

You can launch the web interface of TVB with the following command:

python -m WEB_PROFILE tvb.config

Your port 8080 should be free, as a CherryPy service will try to run there. Your default browser should automatically open http://localhost:8080/ which is the way to interact with TVB Web Graphical Interface.

When using from sources (Pypi or Github, not TVB_Distribution), if you want BCT adapters enabled, you should manually download BCT and set env variable BCT_PATH towards the directory where you unzip BCT, plus also have Octave or Matlab installed with command line API enabled.


For testing our packages, PyTest framework can be used.

Pytest will run all files in the current directory and its subdirectories of the form test_*.py or *

The command for running our tests has two forms:

  1. Recommendation when working with a git clone of this TVB Github repo:

       cd [folder_where_tvb_framework_is]
       pytest tvb/test/framework [--profile=TEST_POSTGRES_PROFILE] [--junitxml=path]
       # default profile value is TEST_SQLITE_PROFILE
       cd [folder_where_tvb_library_is]
       pytest tvb/test/library [--junitxml=path]
  2. The second alternative form of running TVB tests, when installing TVB from Pypi, is:

       pip install -U tvb-framework
       pytest --pyargs tvb.tests.framework
       pip install -U tvb-library
       pytest --pyargs tvb.tests.library


A coverage report can be generated with:

pip install pytest-cov
cd [folder_where_tvb_framework_is]
py.test --cov=tvb tvb/tests/ --cov-branch --cov-report xml:[file_where_xml_will_be_generated]

cd [folder_where_tvb_library_is]
py.test --cov-config .coveragerc --cov=tvb tvb/tests/ --cov-branch --cov-report xml:[file_where_xml_will_be_generated]

Relevant TVB Resources

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