Memmesheimer Group

Our research addresses the microscopic, precise spiking dynamics in neural networks, the le­vel of mesoscopic col­lective dynamics as well as emer­gent large-scale phenomena such as control and lear­ning of behavior. The long-term aim is a theoretical understanding of the complex dynamics and the resulting computations of neural systems. For this goal, it is essential to join strict mathemat­ical approaches and perspectives from neurophysiology and neurobiology and to apply and to fur­ther develop methods from theoretical physics and com­puter science.

What are we offering?

Tools for the understanding of the dynamics and computation in neural systems: theory of computation in neural network, dynamical systems theory, theory of systems with noise.

What are we interested in for collaboration?

Experimental and theoretical collaborations on neural network dynamics and computation in general and in specific brain areas like the hippocampus.

What platforms, analysis tools or facilities do we use and can share?

Several high performance computing devices.

Discover our homepage here.

To learn more about Prof. Dr. Raoul-Martin Memmesheimer, follow him on ORCID.

Methods

• Single neuron modeling
• Neural network modeling
• Numerical simulations of neural networks
• Statistical physics-based analytical descriptions of spiking neural network dynamics
• Nonlinear dynamics-based descriptions of neural rate networks
• Learning and design of artificial neural networks

5 selected papers

1. “P. Züge, C. Klos, and R.-M. Memmesheimer (2023) Weight versus Node Perturbation Learning in Temporally Extended Tasks: Weight Perturbation Often Performs Similarly or Better. Phys. Rev. X, 13, 021006.
2. Y.F. Kalle Kossio, S. Goedeke, C. Klos, and R.-M. Memmesheimer (2021) Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation. Proc. Natl Acad. Sci. USA, 118:e2023832118.
3. L. Pothmann, C. Klos, O. Braganza, S. Schmidt, O. Horno, R.-M. Memmesheimer, and H. Beck (2019) Altered dynamics of canonical feed-back inhibition predicts increased burst transmission in chronic epilepsy. J. Neurosci. 39:8998-9012.
4. L.F. Abbott, B. DePasquale, and R.-M. Memmesheimer (2016) Building functional networks of spiking model neurons. Nat. Neurosci. 19:350-355.
5. R.-M. Memmesheimer, R. Rubin, B. Ölveczky, and H. Sompolinsky (2014) Learning precisely timed spikes. Neuron 82:925-938.”