Navigation and service


The SimLab Neuroscience is organised into five teams:

Large Scale Simulation and Neuromorphic Systems

As neuronal simulations become ever more complex, innovations are required in software and hardware. We are key contributors to the NEST simulator core and infrastructure. Execution performance and maintainability are our primary concerns. Simultaneously, we enhance its usability by providing a convenient way of defining models through the NEST modelling language NESTML.

On the hardware side, we investigate novel neuromorphic systems in the Advanced Computing Architecture project. Modular hardware approaches combining traditional and non-von Neumann architectures promise accelerated simulations of neural networks and new computational paradigms.

Team members: Prof. Dr. Abigail Morrison (lead), Dr. Jochen Martin Eppler, Charl Linssen, Guido Trensch

Research and Software:

Machine Learning and Data Analytics for Neuroimaging

The amount of neuroimaging data to be analysed has increased over the years, pushing traditional workflows to their limits. These workflows include classical image processing methods, complex modelling steps such as diffusion tensor reconstruction, and modern machine learning techniques such as deep learning models. We adapt methods originally developed for work stations or small clusters to scale up on HPC systems at the Jülich Supercomputer Centre.

In addition, we develop, in close cooperation with neuroscientists, new data analytics and machine learning methods for neuroimaging optimized for HPC systems.

Team members: Dr. Kai Krajsek (lead), Rajalekshmi Deepu, Daniel Todt

Research and Software:

Multiscale Simulation and Architectures

Neuroscience problems have characteristic scales ranging from microseconds and microns to decades and meters, requiring the development of performant software capable of bridging these gaps on evolving HPC hardware.

Our team contributes HPC expertise to brain simulation software at the whole brain, point neuron, and morphologically detailed scales. We develop architectural tools for biologically inspired meta-learning, connectivity generation, visualization and optimization, and high-throughput optimization for high-dimensional parameter spaces. We also construct middleware for coordinating the deployment of complex multiscale workflows, enabling interactive steering on HPC resources.

Team members: Prof. Dr. Abigail Morrison (interim team lead), Sandra Diaz, Wouter Klijn, Dr. Anne Küsters, Drs. Ir. Michiel van der Vlag, Kim Sontheimer, Alper Yegenoglu

Research and Software:

Analysis, Visualization and Learning

Our team develops tools that can simplify and accelerate exploratory data analysis, enabling interactive visualization to replace much of the tedious and error prone programming effort typically expended in such projects. We are further involved in applied deep learning projects that use convolutional networks for the segmentation of MRI images and micrographs, as well as artificial recurrent neural networks to study the differences and similarities between the solutions employed by the brain and artificially engineered models.

Team members: Fahad Khalid (lead), Andreas Müller, Tabea Kirchner, Qin Wang

Research and Software:


Coordination, Communication and Project Management

Our team supports the SimLab Neuroscience in all matters related to the coordination, management and dissemination of projects, networks and alliances, in which the SimLab is engaged.

In particular, we are responsible for the management of two major European initiatives at the intersection of neuroscience and high-performance computing: The High Performance Analytics and Computing Platform subproject of the Human Brain Project (HBP), and the Interactive Computing E-Infrastructure (ICEI) project, which is creating the Fenix Infrastructure for the HBP and other science communities.

Team members: Dr. Boris Orth (lead), Dr. Anne Nahm, Dr. Maren Frings, Steffen Graber, Anna Lührs

Projects, networks and alliances: