The Virtual Brain (TVB-HPC)

TVBThe Virtual Brain (TVB) is a large-scale brain simulator. With a community of thousands of users around the world, TVB has become a validated, popular and standard choice for the simulation of whole brain activity. TVB users can create simulations using neural mass models which can produce outputs for different analysis and modalities. TVB allows scientists to explore and analyze both simulated and experimental data. It contains analytic tools for evaluating relevant scientific parameters in light of that data. The current implementation of TVB is written in Python, with limited large-scale parallelization over different parameters. The objective of TVB-HPC is to enable large-scale parallelization of TVB simulations by making use of high performance computing to explore large parameter spaces for the models. With this approach, neuroscientists can define their models in a domain specific language based on NeuroML and automatically generate code which can run either on GPUs or on CPUs with different architectures and optimizations. The result is a framework that hides the complexity of writing robust parallel code and offers neuroscientists a fast and efficient access to high performance computing. TVB-HPC is publicly available on GitHub and, at the end of HBP project phase SGA2, it will be possible to launch large parameter simulations using code automatically generated with this framework via the HBP Collaboratory.

Date of release30.04.2019 (continuous minor releases since then)
Version of softwarev0.1-alpha
Version of documentationv0.1-alpha
Software available
ResponsibleSandra Diaz (JUELICH):
Requirements & dependencies
Target system(s)HPC systems

In Situ Pipeline

This is the newer, more general version of NEST in situ framework.

The in situ pipeline consists of a set of libraries that can be integrated into neuronal network simulators developed by the HBP to enable live visual analysis during the runtime of the simulation. The library called ‘nesci’ (neuronal simulator conduit interface) stores the raw simulation data into a common conduit format and the library called ‘contra’ (conduit transport) transports the serialized data from one endpoint to another using a variety of different (network) protocols. The pipeline currently works with NEST and Arbor. Support for TVB is currently in development.

Prototypical implementation into the HPAC Platform finalised in February 2019.

Date of releaseFirst released in July 2018 with continuous updates (see also above)
Version of software18.07
Version of documentation18.07
Software available
DocumentationSee the readme files in the repositories
ResponsibleRWTH: Simon Oehrl (
Requirements & dependenciesRequired: CMake, C++14, Conduit
Optional: Python, Boost, ZeroMQ
Target system(s)Desktops/HPC Systems running Linux, macOS or Windows


MSPViz is a visualization tool for the Model of Structural Plasticity. It uses a visualisation technique  based on the representation of the neuronal information through the use of abstract levels and a set of schematic representations into each level. The multilevel structure and the design of the representations constitutes an approach that provides organized views that facilitates visual analysis tasks.

Each view has been enhanced adding line and bar charts to analyse trends in simulation data. Filtering and sorting capabilities can be applied on each view to ease the analysis. Other views, such as connectivity matrices and force-directed layouts, have been incorporated, enriching the already existing views and improving the analysis process. This tool has been optimised to lower render and data loading times, even from remote sources such as WebDav servers.

Screenshot of MSPViz
Screenshot of MSPViz
Screenshot of MSPViz
Screenshot of MSPViz
View of MSPViz to investigate structural plasticity models on different levels of abstraction: connectivity of a single neuron
View of MSPViz to investigate structural plasticity models on different levels of abstraction: full network connectivity
Date of releaseMarch 2018
Version of software0.2.6
Version of documentation0.2.6 for users
Software available
DocumentationSelf-contained in the application
ResponsibleUPM: Juan Pedro Brito (
Requirements & dependenciesSelf-contained code
Target system(s)Platform independent


The development of VIOLA was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.

VIOLA (VIsualizer Of Layer Activity) is a tool to visualize activity in multiple 2D layers in an interactive and efficient way. It gives an insight into spatially resolved time series such as simulation results of neural networks with 2D geometry. The usage example shows how VIOLA can be used to visualize spike data from a NEST simulation ( of an excitatory and an inhibitory neuron population with distance-dependent connectivity.

Date of releaseJanuary 2016
Version of softwarenot yet specified
Version of documentationnot yet specified
Software available
ResponsibleU Trier: Weyers, Benjamin (, Forschungszentrum Jülich: Espen Hagen (, Johanna Senk (
Requirements & dependenciesThree.js
Target system(s)Web Browser (Google Chrome)


The development of InDiProv was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.

This server-side tool is meant to be used for the creation of provenance tracks in context of interactive analysis tools and visualization applications. It is capable of tracking multi-view and multiple applications for one user using this ensemble. It further is able to extract these tracks from the internal data base into a XML-based standard format, such as the W3C Prov-Model or the OPM format. This enables the integration to other tools used for provenance tracking and will finally end up in the UP.

Date of releaseAugust 2015
Version of softwareAugust 2015
Version of documentationAugust 2015
Software available
ResponsibleRWTH Aachen: Benjamin Weyers ( and Torsten Kuhlen (
Requirements & dependenciesWritten in C++ , Linux environment, MySQL server 5.6, JSON library for annotation, CodeSynthesis XSD for XML serialization and parsing, ZeroMQ library, Boost library, xercex-c library and mysqlcppcon library
Target system(s)Server-side systems


The development of RTNeuron in the HPAC Platform was co-funded by the HBP during the second project phase (SGA1). This page is kept for reference but will no longer be updated.

RTNeuron is a scalable real-time rendering tool for the visualisation of neuronal simulations based on cable models. Its main utility is twofold: the interactive visual inspection of structural and functional features of the cortical column model and the generation of high quality movies and images for presentations and publications. The package provides three main components:

  • A high level C++ library.
  • A Python module that wraps the C++ library and provides additional tools.
  • The Python application script

A wide variety of scenarios is covered by In case the user needs a finer control of the rendering, such as in movie production or to speed up the exploration of different data sets, the Python wrapping is the way to go. The Python wrapping can be used through an IPython shell started directly from or importing the module rtneuron into own Python programs. GUI overlays can be created for specific use cases using PyQt and QML.

RTNeuron is available on the pilot system JULIA and on JURECA as environment module.

RTNeuron in aixCAVE
RTNeuron in aixCAVE
Neuron rendered by RTNeuron
Neuron rendered by RTNeuron
Visual representation of cell dyes
Simulation playback
Interactive circuit slicing
Connection browsing
Date of releaseFebruary 2018
Version of software2.13.0
Version of documentation2.13.0
Software available; Open sourcing scheduled for June 2018
ResponsibleSamuel Lapere
Requirements & dependenciesBBP SDK, Boost, Equalizer, OpenSceneGraph, osgTransparency, Python, Qt, NumPy, OpenMP, VRPN, Cuda, ZeroEQ
Target system(s)


The development of Livre was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.

Livre is an out-of-core rendering engine that has the following features:

  • Distributed rendering using Equalizer parallel rendering framework
  • Octree based out-of-core rendering.
  • Visualisation of pre-processed UVF format volume data sets.
  • Real-time voxelisation and visualisation of surface meshes using OpenGL 4.2 extensions.
  • Real-time voxelisation and visualisation of Blue Brain Project (BBP) morphologies.
  • Real-time voxelisation and visualisation of local-field potentials in BBP circuit.
  • Multi-node, multi-GPU rendering.
Data rendered with Livre
Data rendered with Livre
Data rendered with Livre
Data rendered with Livre
Date of releaseApril 2015
Version of software0.3
Version of documentation0.3
Software available
ResponsibleEPFL: Stefan Eilemann (
Requirements & dependenciesOpenMP, Tuvok, ZeroEQ, FlatBuffers, Boost, Equalizer, Collage, Lunchbox, dash, OpenGL, PNG, Qt
Target system(s)


The development of DisplayCluster was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.

DisplayCluster is a software environment for interactively driving large-scale tiled displays. It provides the following functionality:

  • View media interactively such as high-resolution imagery, PDFs and video.
  • Receive content from remote sources such as laptops, desktops or parallel remote visualization machines using the Deflect library.
Display Cluster
DisplayCluster on a mobile tiled display wall
DisplayCluster on a tiled display wall
DisplayCluster on a tiled display wall
Date of release2013
Version of software0.5
Version of documentation0.5
Software available
ResponsibleEPFL, Stefan Eilemann (
Requirements & dependenciesBoost, LibJPEGTurbo, Qt, GLUT, OpenGL, Lunchbox, FCGI, FFMPEG, MPI, Poppler, TUIO, OpenMP
Target system(s)Tiled display walls