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.

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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

  1. 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.
  2. C. Klos, Y.F. Kalle Kossio, S. Goedeke, A. Gilra, R.-M. Memmesheimer (2020) Dynamical learning of dynamics Phys. Rev. Lett. 125:088103.
  3. L.F. Abbott, B. DePasquale, and R.-M. Memmesheimer (2016) Building functional networks of spiking model neurons Nat. Neurosci. 19:350-355.
  4. R.-M. Memmesheimer, R. Rubin, B. Ölveczky, and H. Sompolinsky (2014) Learning precisely timed spikes Neuron 82:925-938.
  5. 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.