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
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Latest Publications
Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
Senk J., Hagen E., van Albada S.J., Diesmann M.
Cerebral Cortex 34(10)
10.1093/cercor/bhae405
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.
Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information.
Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region