Navigation and service

Research

Simulation engineering

Simulation science

Data Analysis and visualisation

Neuroimaging pipelines

Previous research topics

Simulation engineering

    Performance Modeling for the Neural Simulator NEST

    Performance modeling for the neural simulator NEST

    Our work aims at developing performance models for the simulation stage of NEST by a semi-empirical approach. We collect measurements of the runtime performance of NEST under varying parameter settings, and subsequently fit theoretical models to this data.

    More: Performance modeling for the neural simulator NEST …

    NESTML

    NESTML - A modeling language for spiking neuron and synapse models for NEST

    NESTML is a modular and extensible domain specific language which provides neuroscience domain concepts as first-class language constructs and supports domain experts in creating neuron models for the neural simulation tool NEST.

    More: NESTML - A modeling language for spiking neuron and synapse models for NEST …

    SpiNNaker

    SpiNNaker Support for the Neural Simulator NEST

    The aim of the project is to add the SpiNNaker hardware to the platforms supported by NEST. Exact reproducibility and correctness of simulation results are important.

    More: SpiNNaker Support for the Neural Simulator NEST …

    NEST I/O Performance Logo

    I/O Performance for the NEST Neural Simulator

    The NEST simulator for spiking neuronal networks is meant to be a scalable simulator: it is designed to function on laptops, clusters, current petascale supercomputers and beyond to emerging exascale architectures. At extreme scales, issues of memory utilization and network size have emerged as challenges to be solved

    More: I/O Performance for the NEST Neural Simulator …

    Structural Plasticity Logo

    Structural Plasticity

    Structural plasticity in a neural network refers to the physical creation and deletion of synapses. This effect is present during brain development, learning and healing after lesions.

    More: Structural Plasticity …

    Arbor

    Arbor

    Arbor is a performance portable library for the simulation of large networks of multi-compartment, morphologically detailed neurons on emerging HPC architectures. It is developed under an open development model (https://github.com/arbor-sim/arbor) by the Jülich Supercomputing Centre’s SimLab Neuroscience and the Swiss National Supercomputing Center (CSCS), in close collaboration with the neuroscientific community.

    More: Arbor …

    Jülich Parameter Exploration (JUPeX)

    Jülich Parameter Exploration (JuPeX)

    Carried out in collaboration with Prof. Wolfgang Maass's research group at the Technical University of Graz, this project enables large scale parameter space exploration and optimization of simulations on HPC systems. By extending the UNICORE and JUBE frameworks developed at the JSC, we will provide a flexible platform for parameter exploration using well known optimization algorithms.

    More: Jülich Parameter Exploration (JuPeX) …

    Modular Science

    Modular Science

    The modular science framework is a software for the deployment of complex interactive workflows on supercomputers, which serves as an orchestrator for scientific applications.

    More: Modular Science …

    Validation methodology for network simulations

    Validation methodology for network simulations

    In this project, we are investigating methods for quantitative and qualitative validation of neuron models and neural network simulations.

    More: Validation methodology for network simulations …

    Advanced Computing Architectures (ACA): accelerated neural network simulations

    Accelerated neural network simulations: novel neuromorphic system architecture approaches

    In this project, we are exploring hard- and software architectural approaches to identify opportunities to accelerate simulations of complex neural networks.

    More: Accelerated neural network simulations: novel neuromorphic system architecture approaches …

    High Performance Computing for the Virtual Brain (HPC-TVB)

    High Performance Computing for the Virtual Brain

    In this project, our goal is to enhance the general performance of simulation and analysis tools provided by "The Virtual Brain" (TVB). In collaboration with the research group of Dr. Viktor Jirsa at Aix-Marseille Université, we are working on the development of a modular framework for the implementation of TVB neural mass models on HPC resources including automatic code generation schemes to efficiently port TVB models between computing architectures.

    More: High Performance Computing for the Virtual Brain …

Go to

Simulation engineering Simulation science Data Analysis and visualisation Neuroimaging pipelines
Previous research topics

Simulation science

    Gap Junction

    Unified framework for spiking and gap-junction interactions

    In the unified framework project, the Jacobi waveform relaxation method is used to solve gap-junction interactions between neurons in the Nest simulator (Hahne et al., 2015). The implemented algorithm based on this method allows for a tradeoff between numerical precision and communication size between neurons in the simulation. 

    More: Unified framework for spiking and gap-junction interactions …

    Virtual Connectome CorrelationMatrix

    Dynamic structure for the Virtual Connectome

    The Virtual Brain (TVB) is a "framework for the simulation of the dynamics of large-scale brain networks with biologically realistic connectivity". Tractography data can be combined with a variety of neural mass models in order to predict experimental and clinical observables such as local field potential, EEG and fMRI measures.

    More: Dynamic structure for the Virtual Connectome …

Go to

Simulation engineering Simulation science Data Analysis and visualisation Neuroimaging pipelines
Previous research topics

Data Analysis and Visualization

    thumbnail

    VIGAN: Visualization-guided Interactive Graph Analysis

    VIGAN is a tool for high performance analysis and interactive multi-view visualization of graph/network data used in neuroscience research. It incorporates a wide variety of analysis and visualization features from existing tools and expands the applicability of graph analysis to a broader set of use cases, as well as larger datasets, by providing novel functions.

    More: VIGAN: Visualization-guided Interactive Graph Analysis …

    Interactive steering and visualization

    Interactive steering and visualization

    We have developed a steering and visualization tool which allows scientists to interact with NEST simulations during run time. The interaction consists of the possibility to change parameters of the network and visualize the effect of these changes in the simulation.The tool is based on the ‘nett’ software framework developed at RWTH Aachen University.

    More: Interactive steering and visualization …

    ViCoGen

    Visual Connectivity Generation

    There is a gap between experimental descriptions of connectivity in neuronal networks and information which can be used by simulators to generate network connectivity. With this project, we aim to generate a visual language for connectivity and a framework which can serve as a bridge between experimental datasets and simulation models. Empirically derived data and theoretical models expressed in a shared visual representation called NeuroScheme are used to generate connections for simulation or further processing.

    More: Visual Connectivity Generation …

Go to

Simulation engineering Simulation science Data Analysis and visualisation Neuroimaging pipelines
Previous research topics

Neuroimaging pipelines

Go to

Simulation engineering Simulation science Data Analysis and visualisation Neuroimaging pipelines
Previous research topics

Previous research topics

    thumbnail

    Calibration of 3D-PLI measurements

    In this project, we contribute algorithmic improvements and high performance implementations thereof, to the 3D-PLI calibration process.

    More: Calibration of 3D-PLI measurements …

    MLECR Icon

    Maximum Likelihood Estimation based Connection Reconstruction (MLECR)

    Understanding the synaptic connections and organization of neural networks is a key step in understanding the dynamics of the brain. There are two challenges: simple models are often unable to resolve ambiguities and complex models are computationally very expensive. The MLECR method achieves good performance in both these problem domains.

    More: Maximum Likelihood Estimation based Connection Reconstruction (MLECR) …

    Fiber orientations

    Quality assurance for the 3D-PLI workflow

    In this project we develop methods and tools that help identify errors at various stages of the complex Three-dimensional Polarized Light Imaging (3D-PLI) workflow.

    More: Quality assurance for the 3D-PLI workflow …

    Slice of a brain - registered

    Using structure tensor analysis to measure PLI image registration quality

    In this project, we employ the Structure Tensor Analysis (STA) method to verify if the 3D reconstruction of the brain from images of the thinly sliced sections is accurate enough for use in further research.

    More: Using structure tensor analysis to measure PLI image registration quality …