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.
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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
- Y.F. Kalle Kossio, S. Goedeke, C. Klos, R.-M. Memmesheimer (2021) Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation Proc. Natl Acad. Sci. USA, 118:e2023832118.
- C. Klos, Y.F. Kalle Kossio, S. Goedeke, A. Gilra, R.-M. Memmesheimer (2020) Dynamical learning of dynamics Phys. Rev. Lett. 125:088103.
- L.F. Abbott, B. DePasquale, and R.-M. Memmesheimer (2016) Building functional networks of spiking model neurons Nat. Neurosci. 19:350-355.
- R.-M. Memmesheimer, R. Rubin, B. Ölveczky, and H. Sompolinsky (2014) Learning precisely timed spikes Neuron 82:925-938.
- R.-M. Memmesheimer (2010). Quantitative prediction of intermittent high-frequency oscillations in neural networks with supralinear dendritic interactions Proc. Natl Acad. Sci. USA, 107:11092-11097.
