Tag Archives: interactive visualization and analysis

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 availablehttps://devhub.vr.rwth-aachen.de/VR-Group/contra
DocumentationSee the readme files in the repositories
ResponsibleRWTH: Simon Oehrl (oehrl@vr.rwth-aachen.de)
Requirements & dependenciesRequired: CMake, C++14, Conduit
Optional: Python, Boost, ZeroMQ
Target system(s)Desktops/HPC Systems running Linux, macOS or Windows


VTK-m is a scientific visualization and analysis framework that offers a wealth of building blocks to create visualization and analysis applications. VTK-m facilitates scaling those applications to massively parallel shared memory systems, and it will – due to its architecture – most likely also run efficiently on future platforms.

HPX is a task-based programming model. It simplifies the formulation of well-scaling, highly-parallel algorithms. Integrating this programming model into VTK-m streamlines the formulation of its parallel building blocks and thus makes their deployment on present and emerging HPC platforms more efficient. Since neuroscientific applications require more and more compute power as well as memory, harnessing the available resources will become a challenge in itself. By combining VTK-m and HPX into task-based analysis and visualization, we expect to provide suitable tools to effectively face this challenge and facilitate building sophisticated interactive visual analysis tools, tailored to the neuroscientists’ needs.

Parallel primitive algorithms required for VTK-m have been added to HPX along with API support to enable the full range of visualization algorithms developed for VTK-m. A new scheduler has been developed that accepts core/numa placement hints from the programmer such that cache reuse can be maximized and traffic between sockets minimized. High performance tasks that access data shared by application and visualization can use this capability to improve performance. The thread pool management was improved to allow visualization tasks, communication tasks, and application tasks to execute on different cores if necessary, which reduces latency between components and improves the overall throughput of the distributed application. RDMA primitives have been added to the HPX messaging layer. These improvements make it possible to scale HPX applications to very high node/core counts. Respective tests have been successful on 10k nodes using 650k cores.

Date of releaseNovember 2017
Version of software1.1
Version of documentation1.1
Software availablehttps://gitlab.kitware.com/vtk/vtk-m
ResponsibleJohn Biddiscombe (EPFL): john.biddiscombe@epfl.ch
Requirements & dependencies
Target system(s)HPC platforms


The PLIViewer is visualization software for 3D-Polarized Light Imaging (3D-PLI), to interactively explore the scalar and vector datasets; it provides additional methods to transform data, thus revealing new insights that are not available in the raw representations. The high resolution provided by 3D-PLI produces massive, terabyte-scale datasets, which makes visualization challenging.

The PLIViewer tackles this problem by providing functionality to select areas of interests from the dataset, and options for downscaling. It makes it possible to interactively compute and visualize Orientation Distribution Functions (ODFs) and polar plots from the vector field, which reveal mesoscopic and macroscopic scale information from the microscopic dataset without significant loss of detail. The PLIViewer equips the neuroscientist with specialized visualization tools needed to explore 3D-PLI datasets through direct and interactive visualization of the data.


The original dataset: Fibre Orientation Maps rendered on top of Retardation map
A full slice from the Fibre Orientation Map of a Vervet Monkey
Orientation Distribution Functions (ODFs) rendered with Streamline Tractography
Close-up view of the ODFs
Date of releaseFebruary 2018
Version of software
Version of documentation1.1.0
Software availablehttp://https://devhub.vr.rwth-aachen.de/VR-Group/pli_vis
ResponsibleAli Demiralp: demiralp@vr.rwth-aachen.de
Requirements & dependencies
Target system(s)

NEST-simulated spatial-point-neuron data visualisation

Complementary to other viewers and visualization implementations for NEST simulations, this technology offers a rendering of activity and membrane potentials in a neural network simulated with NEST.

A prototypical implementation exists that is based on vtk, the widely-used visualisation toolkit. This implementation can be generally run by computational neuroscientists on their workstations, imposing only moderate hardware requirements. Experiments using rendering on high-performance computing infrastructure were successful.

The results indicate that this component is extensible towards large-scale simulations that require HPC resources and thus produce large output data. The hi-fidelity rendering used in this case provides very high quality images that may be suitable for publications (Proof of concept image below).

Rendering of color-coded membrane potentials on spatial neurons from a running NEST simulation
Proof of concept of a high-quality rendering of spatially organized point neurons
Date of release
Version of softwarePlease contact the developers
Version of documentation
Software availablePlease contact the developers
DocumentationAvailable on demand
ResponsibleThomas Vierjahn
Requirements & dependencies
Target system(s)workstations to HPC

Multi-View Framework

The Multi-View Framework is a software component, which offers functionality to combine various visual representations of one or more data sets in a coordinated fashion.  Software components offering visualization capabilities can be included in such a network, as well as software components offering other functionality, such as statistical analysis. Multi-display scenarios can be addressed by the framework as coordination information can be distributed over network between view instances running on distributed machines.

The framework is composed of three libraries: nett, nett-python and nett-connect. nett implements a light-weight underlying messaging layer enabling the communication between views, whereas nett-python implements a python binding for nett, which enables the integration of python-based software components into a multi-view setup. nett-connect adds additional functionality to this basic communication layer, which enables non-experts to create multi-view setups according to their specific needs and workflows.

Interactive optimization of parameters for structural plasticity in neural network models (top left); comparative analysis of NEST simulations (top right); statistical analysis of NEST simulations (bottom left); multi-device and multi-user scenarios (bottom right)
Date of release2017
Version of softwareN/A
Version of documentationN/A
Software availablePlease contact the developers
ResponsibleU Trier: Weyers, Benjamin (weyers@uni-trier.de)
Requirements & dependencies
Target system(s)


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

VisNEST is a tool for visualizing neural network simulations of the macaque visual cortex. It allows for exploring mean activity rates, connectivity of brain areas and information exchange between pairs of areas. In addition, it allows exploration of individual populations of each brain area and their connectivity used for simulation.

VisNEST screenshot
Date of releaseInformation available on demand.
Version of softwareInformation available on demand.
Version of documentationInformation available on demand.
Software availableNot publicly available yet. Please contact the developers in case of interest.
DocumentationReference paper:
Nowke, Christian, Maximilian Schmidt, Sacha J. van Albada, Jochen M. Eppler, Rembrandt Bakker, Markus Diesrnann, Bernd Hentschel, and Torsten Kuhlen. "VisNEST—Interactive analysis of neural activity data." In Biological Data Visualization (BioVis), 2013 IEEE Symposium on, pp. 65-72. IEEE, 2013.
ResponsibleRWTH Aachen: Benjamin Weyers (weyers@vr.rwth-aachen.de) and Torsten Kuhlen (kuhlen@vr.rwth-aachen.de)
Requirements & dependenciesViSTA, boost, zmq, hdf5
Target system(s)High Fidelity Visualization Platforms, Immersive Visualization Hardware, Desktop Computers