Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience

Welcome to the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience

The brain is a highly complex organ and ubiquitous in our daily lives. However, little is understood about it or its functions. Undertaking the study of this organ is a challenging and fascinating endeavour and can spawn new technologies and alternative methods of treatment of diseases. Research at the Institute of Computational and Systems Neuroscience encompasses theoretical, data-analytic and simulation approaches to develop multi-scale models of the brain. It is our firm belief that progress in understanding a complex system like the brain can only be achieved through this multi-faceted approach.

Directors: Prof. Dr. Sonja Grün and Prof. Dr. Markus Diesmann

Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience

Computational Neurophysics

The focus of the group of Markus Diesmann are bottom-up approaches in order to integrate physiological and anatomical data into models, in particular model development, theory of neuronal networks, and correlation dynamics. This also requires the development of simulation technology for neural networks.

Lead: Markus Diesmann, Director of IAS-6

Statistical Neuroscience

The research group of Sonja Grün focuses on the development of analysis strategies and tools that uncover concerted activity in electrophysiological signals from the brain.

Lead: Sonja Grün, Director of IAS-6

Computation in Neural Circuits

Investigation of mechanisms underlying neural computation through the development of models on the level of networks of spiking neurons. The group of Abigail Morrison applies a predominantly top-down approach to discover functional constraints on structure, plasticity and dynamics, particularly with respect to learning and memory.

Lead: Abigail Morrison

Theory of Multi-Scale Neuronal Networks

Moritz Helias' group focuses on mechanisms that shape the dynamics and information processing in biological and artificial neuronal networks. On the side of biological networks, we are interested in the relationship between the structure and dynamics of neural networks to unveil experimentally testable mechanisms of collective phenomena. For artificial neuronal networks, we develop the physics of AI that allows us to understand and quantify generalization properties and learning. Employing and developing statistical physics methods to formulate biological and artificial networks in a unified language, allows us to discover overarching principles of information processing and to detect and quantify qualitative differences.

Lead: Moritz Helias

Theoretical Neuroanatomy

The work group led by Sacha van Albada studies the architecture and connectivity of brain circuits as the basis for neural network models at the resolution of neurons and synapses that relate structure to dynamics.

Lead: Sacha van Albada

Team Data Science of Electro- and Optophysiology in Behavioural Neuroscience

The team of Michael Denker addresses the practical challenges that arise in the context of data management and reproducible data analytics in neuroscience by applying concepts from physics and computer science.

Lead: Michael Denker

Team Future Simulation Architectures

Embracing the evolving landscape of high-performance computing systems, the team of Johanna Senk identifies challenges and explores solutions for neural network simulation technology.

Lead: Johanna Senk

News and Events

NEST Conference 2026

We organize the annual NEST Conference which provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application. Registration deadline: Sunday, June 14 (23:59 CEST), 2026!

Neural Networks

Spiking neural networks reach a new level

Three studies recently published by the journal Cerebral Cortex present new models of spiking networks in the brain. Electrical spikes of neurons are at the core of neural information processing. With millions of neurons and billions of synapses, the new generation of open models are powerful tools to study the complex dynamics in large networks, with implications for basic neuroscience, neuromorphic computing and AI.

Latest Publications

A biologically plausible recurrent spiking architecture for modeling nonlinear dynamic mechanical systems

How Heterogeneity Shapes Dynamics and Computation in the Brain

Strong and localized recurrence controls the dimensionality of neural activity across brain areas

Scalable construction of spiking neural networks using up to thousands of GPUs

A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks

Event-driven eligibility propagation in large sparse networks

Exploiting network topology in brain-scale simulations of spiking neural networks

The effect of data preprocessing on spike correlation analysis results

Detection and remocal of hyper-synchronous artifacts in massively parallel spike recordings

A quality measure for repeating multiple-unit spike patterns