Theory of Multi-scale Neuronal Networks group

About
The focus of this group is the investigation of mechanisms that shape the dynamics and function of biological and artificial neuronal networks. Starting either from analyses of electrophysiological data of parallel spiking activity in vivo or activity of network models, the group aims to infer fundamental relationships between the structure and dynamics of neural networks to unveil experimentally testable mechanisms underlying observed collective phenomena. A further research goal is to study how these dynamics are employed by the circuit to implement information processing. To analyze both the dynamic and the functional mechanisms the group employs and transfers methods from theoretical physics and makes them applicable to neural systems.
Research Topics
dynamic mechanisms in neural networks, mechanisms of neural information processing, methods transfer and adaption from theoretical physics, field theory of neuronal networks
research Foci

Dynamic Mechanisms in Neural Networks
- Oscillations
- Statisitcs of correlations
- Dimensionality
- Chaos
Mechanisms of Neural Information Processing
- Biological and artificial neural networks
- Discrete vs continuous communication
- Feedforward vs recurrent networks
Methods Transfer and Adaptation from Theoretical Physics
- Statistical field theory
- Theory of disordered systems
- Methods from Statisitcal Physics (Fokker Planck theory, Edgeworth expansion, etc.)
Members
Collaborations (external)
Tobias Kühn
Alexandre Rene
Stefano Recanatesi
Eric Shea-Brown
Gabriel K. Ocker
Xiaoxuan Jia
Luke Campagnola
Tim Jarsky
Stephanie Seeman
Alexa Riehle
Thomas Brochier
Lukas Deutz
Nicole Voges
Paulina Dabrowska
Michael von Papen
Andrea Cristanti
Funding
Helmholtz networking fund
BMBF
DFG Excellence initiative (ERS RWTH)