GUI tool.

PLIViewer

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

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
Documentationhttp://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

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

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

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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
Documentationhttps://devhub.vr.rwth-aachen.de/cnowke/nett-connect
ResponsibleU Trier: Weyers, Benjamin (weyers@uni-trier.de)
Requirements & dependencies
Target system(s)

MSPViz

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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 availablehttp://gmrv.es/mspviz
DocumentationSelf-contained in the application
ResponsibleUPM: Juan Pedro Brito (juanpedro.brito@upm.es)
Requirements & dependenciesSelf-contained code
Target system(s)Platform independent

neuroFiReS

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

neuroFiReS is a library for performing search and filtering operations using both data contents and metadata. These search operations will be tightly coupled with visualization in order to improve insight gaining from complex data. A first prototype (named spineRet) for searching and filtering over segmented spine data has been developed.

SpineRet
SpineRet screenshot
Date of releaseN/A
Version of software0.1
Version of documentation0.1
Software availablePlease contact the developers.
DocumentationPlease contact the developers.
ResponsibleURJC: Pablo Toharia (pablo.toharia@urjc.es)
Requirements & dependenciesQt, OpenSceneGraph
Supported OS: Windows 7/ 8.1, Linux (tested on Ubuntu 14.04) and Mac OSX
Target system(s)Desktop computers, notebooks

NeuroScheme

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NeuroScheme is a tool that allows users to navigate through circuit data at different levels of abstraction using schematic representations for a fast and precise interpretation of data. It also allows filtering, sorting and selections at the different levels of abstraction. Finally it can be coupled with realistic visualization or other applications using the ZeroEQ event library developed in WP 7.3.

This application allows analyses based on a side-by-side comparison using its multi-panel views, and it also provides focus-and-context. Its different layouts enable arranging data in different ways: grid, 3D, camera-based, scatterplot-based or circular. It provides editing capabilities, to create a scene from scratch or to modify an existing one.

ViSimpl, part of the NeuroScheme framework, is a prototype developed to analyse simulation data, using both abstract and schematic visualisations. This analysis can be done visually from temporal, spatial and structural perspectives, with the additional capability of exploring the correlations between input patterns and produced activity.

 

NeuroScheme
NeuroScheme screenshot
NeuroScheme
NeuroScheme screenshot
NeuroScheme
NeuroScheme screenshot
NeuroScheme
NeuroScheme screenshot
NeuroScheme
NeuroScheme screenshot
Overview of various neurons
User interface of ViSimpl visualising activity data emerging from a simulation of a neural network model
Date of releaseMarch 2018
Version of software0.2
Version of documentation0.2
Software availablehttps://github.com/gmrvvis/NeuroScheme
Documentationhttps://github.com/gmrvvis/NeuroScheme, http://gmrv.es/gmrvvis
ResponsibleURJC: Pablo Toharia (pablo.toharia@urjc.es)
Requirements & dependenciesRequired: Qt4, nsol
Optional: Brion/BBPSDK (to access BBP data), ZeroEQ (to couple with other software)
Supported OS: Windows 7, Windows 8.1, Linux (tested on Ubuntu 14.04) and Mac OSX
Target system(s)Desktop computers, notebooks, tablets

NeuroLOTs

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NeuroLOTs is a set of tools and libraries that allow creating neuronal meshes from a minimal skeletal description. It generates soma meshes using FEM deformation and allows to interactively adapt the tessellation level using different criteria (user-defined, camera distance, etc.)

NeuroTessMesh provides a visual environment for the generation of 3D polygonal meshes that approximate the membrane of neuronal cells, starting from the morphological tracings that describe neuronal morphologies. The 3D models can be tessellated at different levels of detail, providing either a homogeneous or an adaptive resolution of the model. The soma shape is recovered from the incomplete information of the tracings, applying a physical deformation model that can be interactively adjusted. The adaptive refinement process performed in the GPU generates meshes, that allow good visual quality geometries at an affordable computational cost, both in terms of memory and rendering time. NeuroTessMesh is the front-end GUI to the NeuroLOTs framework.

Related Publication:
Garcia-Cantero et al. (2017) Front NeuroinDOI: https://dx.doi.org/10.3389/fninf.2017.00038
NeuroLOTs
NeuroLOTs screenshot
NeuroLOTs
NeuroLOTs screenshot
NeuroLOTs
NeuroLOTs screenshot
NeuroLOTs
NeuroLOTs screenshot

Date of releaseNeurolots 0.2.0, March 2018; NeuroTessMesh 0.0.1, March 2018
Version of softwareNeurolots 0.2.0, NeuroTessMesh 0.0.1
Version of documentationNeurolots 0.2.0, NeuroTessMesh 0.0.1
Software availablehttps://github.com/gmrvvis/neurolots, https://github.com/gmrvvis/NeuroTessMesh
Documentationhttps://github.com/gmrvvis/neurolots, https://github.com/gmrvvis/NeuroTessMesh, https://gmrvvis.github.io/doc/neurolots/, https://github.com/gmrvvis/neurolots/blob/master/README.md, http://gmrv.es/neurotessmesh/NeuroTessMeshUserManual.pdf, http://gmrv.es/gmrvvis/neurolots/
ResponsibleURJC: Pablo Toharia (pablo.toharia@urjc.es)
Requirements & dependenciesRequired: Eigen3, OpenGL (>= 4.0), GLEW, GLUT, nsol
Optional: Brion/BBPSDK (to access BBP data), ZeroEQ (to couple with other software)
Supported OS: Windows 7/8.1, GNU/Linux (tested on Ubuntu 14.04) and Mac OSX
Target system(s)High fidelity displays, desktop computers, notebooks

VisNEST

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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
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

Remote Connection Manager (RCM)

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The development of Remote Connection Manager (RCM) was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.

The Remote Connection Manager (RCM) is an application that allows HPC users to perform remote visualisation on Cineca HPC clusters.

The tool offers to

  • Visualize the data produced on Cineca’s HPC systems (scientific visualization);
  • Analyse and inspect data directly on the systems;
  • Debug and profile parallel codes running on the HPC clusters.

The graphical interface of RCM allows the HPC users to easily create remote displays and to manage them (connect, kill, refresh).

Screenshot of RCM
Screenshot of Remote Connection Manager (RCM)
Screenshot of RCM
Screenshot of Remote Connection Manager (RCM)
Date of releaseApril 2015
Version of software1.2
Version of documentation1.2
Software availablehttp://www.hpc.cineca.it/content/remote-visualization-rcm
Documentationhttp://www.hpc.cineca.it/content/remote-visualization-rcm
ResponsibleRoberto Mucci (superc@cineca.it)
Requirements & dependenciesThe “Remote Connection Manager” works on the following operating systems: Windows, Linux, Mac OSX
(OSX Mountain Lion users need to install XQuartz: http://xquartz.macosforge.org/landing/)
Target system(s)Notebooks, office computers

Paraver

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

Paraver is a very flexible data browser. The metrics used are not hardwired on the tool but can be programmed. To compute them, the tool offers a large set of time functions, a filter module, and a mechanism to combine two timelines. This approach allows displaying a huge number of metrics with the available data. The analysis display allows computing statistics over any timeline and selected region, what allows correlating the information of up to three different time functions. To capture the expert’s knowledge, any view or set of views can be saved as a Paraver configuration file. Therefore, re-computing the same view with new data is as simple as loading the saved file. The tool has been demonstrated to be very useful for performance analysis studies, giving much more details about the applications behaviour than most performance tools available.

Screenshot of Paraver
Screenshot of Paraver

The new version 4.6.0 (3rd February 2016) provides the following new features (externally funded) as compared to version 4.5.6 (February 2015) that was part of the HBP-internal Platform Release in M18:

  • Automatic workspaces on trace loading
  • Scalability improvements for traces with more than 64K rows
  • Support for wxWidgets 3
  • Traces with same hierarchy can be combined to analyze
  • External tools integration

The new version 4.6.3 (16th November 2016) provides the following new features:

  • Added punctual information view to timelines
  • Added external tool Run->Spectral from timelines
  • Trace load time reduced by 25%
  • Histogram new features: show only totals and short/long column labels
  • Run app dialog usability improvements
Date of release16th of November 2016
Version of software4.6.3
Version of documentation3.1 (Old, year 2001) But Tutorials available for newer versions
Software availablehttps://tools.bsc.es/downloads
DocumentationParaver website: https://tools.bsc.es/paraver
Documentation: https://tools.bsc.es/tools_manuals
ResponsibleBSC Performance Tools Group: tools@bsc.es
Requirements & dependenciesBoost >= 1.36; Zlib; wxWidgets >= 2.8.0; wxPropertyGrid >= 1.4.0
Target system(s)Any Unix/Linux system (supercomputers, clusters, servers, workstations, laptops, …)