P. Manz and R.-M. Memmesheimer (2022)
Purely STDP-based assembly dynamics: stability, learning, overlaps, drift and aging
bioRxiv
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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.
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W. Braun and R.-M. Memmesheimer (2022)
High-frequency oscillations and replay in a two-population model of hippocampal region CA1
PLoS Comput. Biol., 18:e1009891.
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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, .
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C. Klos, Y.F. Kalle Kossio, S. Goedeke, A. Gilra, and R.-M. Memmesheimer (2020)
Dynamical learning of dynamics
Phys. Rev. Lett. 125:088103.
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F. Pallasdies, S. Goedeke, W. Braun, and R.-M. Memmesheimer (2019)
From Single Neurons to Behavior in the Jellyfish Aurelia aurita
eLife 8:e50084.
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, , , , ,
(2019)
Altered dynamics of canonical feed-back inhibition predicts increased
burst transmission in chronic epilepsy
J. Neurosci. 39:8998-9012.
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P. Manz, S. Goedeke, and R.-M. Memmesheimer (2019)
Dynamics and computation in mixed networks containing neurons that
accelerate towards spiking
Phys. Rev. E 100:042404.
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Y.F. Kalle Kossio, S. Goedeke, B. van den Akker, B. Ibarz, and R.-M. Memmesheimer
(2018)
Growing critical: Self-organized criticality in a developing neural system
Phys. Rev. Lett. 121:058301.
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A. Viriyopase, R.-M. Memmesheimer, and S. Gielen (2018)
Analyzing the competition of gamma rhythms with delayed pulse-coupled oscillators
in phase representation
Phys. Rev. E, 98:022217.
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L.F. Abbott, B. DePasquale, and R.-M. Memmesheimer (2016)
Building functional networks of spiking model neurons
Nat. Neurosci. 19:350-355.
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A. Viriyopase, R.-M. Memmesheimer, and S. Gielen (2016)
Cooperation and competition of gamma oscillation mechanisms
J. Neurophysiol. 116:232-251.
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D. Thalmeier, M. Uhlmann, B. Kappen, and R.-M. Memmesheimer (2016)
Learning universal computations with spikes
PLoS Comput. Biol. 12:e1004895.
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S. Jahnke, M. Timme, and R.-M. Memmesheimer (2015)
A unified dynamic model for learning, replay and sharp-wave/ ripples
J. Neurosci. 35:16236-16258.
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S. Jahnke, R.-M. Memmesheimer, and M. Timme (2014)
Oscillation-induced signal transmission and gating in neural circuits
PLoS Comput. Biol. 10:e1003940.
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R.-M. Memmesheimer*, R. Rubin*, B. Ölveczky, and H. Sompolinsky (2014)
Learning precisely timed spikes
Neuron 82:925-938.
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D. Breuer, M. Timme, and R.-M. Memmesheimer (2014)
Statistical physics of neural systems with non-additive dendritic coupling
Phys. Rev. X 4:011053.
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S. Jahnke, R.-M. Memmesheimer, and M. Timme (2014)
Hub-activated signal transmission in complex networks
Phys. Rev. E 89:030701(R).
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S. Jahnke, R.-M. Memmesheimer, and M. Timme M (2013).
Propagating synchrony in feed-forward networks.
Front. Comput. Neurosci. 7:153.
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S. Jahnke, M. Timme, and R.-M. Memmesheimer (2012).
Guiding synchrony through random networks
Phys. Rev. X 2:041016.
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R.-M. Memmesheimer and M. Timme (2012).
Non-additive coupling enables propagation of synchronous spiking activity in purely random networks
PLoS Comput. Biol., 8:e1002384.
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R.-M. Memmesheimer and M. Timme (2010).
Stable and unstable periodic orbits in complex networks of spiking neurons with delays
Discr. Cont. Dyn. Sys., 28:1555-1588.
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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.
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S. Jahnke, R.-M. Memmesheimer and M. Timme (2009).
How Chaotic is the Balanced State?
Front. Comput. Neurosci., 3:13.
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R.-M. Memmesheimer and M. Timme (2009).
Synchrony and precise timing in complex neural networks
in Handbook on Biological Networks
eds. Y. Moreno, V. Latora, and S. Boccaletti, World Scientific, London.
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S. Jahnke, R.-M. Memmesheimer, and M. Timme (2008).
Stable Irregular Dynamics in Complex Neural Networks
Phys. Rev. Lett. 100:048102.
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R.-M. Memmesheimer (2007).
Precise Spike Timing in Complex Neural Networks
Doctor of Science Thesis, Georg August University Göttingen.
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R.-M. Memmesheimer and M. Timme (2006).
Designing complex networks
Physica D 224:182-201.
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R.-M. Memmesheimer and M. Timme (2006).
Designing the Dynamics of Spiking Neural Networks
Phys. Rev. Lett. 97:188101.
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R.-M. Memmesheimer and G. Schäfer (2005).
Third post-Newtonian constrained canonical dynamics for binary point masses in harmonic coordinates
Phys. Rev. D 71:044021.
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R.-M. Memmesheimer (2004).
Canonical Formulation of Higher Order post-Newtonian/post-Coulombian Point Particle
Dynamics in Harmonic/Lorentz Gauge
Diploma Thesis, Friedrich Schiller University Jena.
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R.-M. Memmesheimer, A. Gopakumar, and G. Schäfer (2004).
Third post-Newtonian accurate generalized quasi-Keplerian parametrization for compact binaries in eccentric orbits
Phys. Rev. D 70:104011.
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Neural network dynamics and computation 