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

News and Events
No results found.
Loading
Latest Publications
NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules.
Linssen C., Babu PN., Eppler JM., Koll L., Rumpe B., Morrison A.
Front. Neuroinform. 19:1544143
Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch
Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
MEBRAINS 1.0: a new population-based macaque atlas.
The Neuron vs the Synapse: Which One Is in the Driving Seat?
Linking network- and neuron-level correlations by renormalized field theory
Machine learning inspired models for Hall effects in non-collinear magnets.
Spurious self-feedback of mean-field predictions inflates infection curves.
Multi-Scale Spiking Network Model of Human Cerebral Cortex
A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity.