Marius Yamakou

• 04/2021 – now: Researcher and Principal Investigator, Dept. of Data Science, University of Erlangen-Nürnberg, Germany
• 11/2019 – 03/2021: Postdoc, Department of Mathematics, University of Erlangen-Nürnberg, Germany
• 02/2018 – 10/2019: Postdoc, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany

• 01/02/2023 – 10/02/2023: Visiting Researcher, Perimeter Institute for Theoretical Physics, Waterloo, Canada
• 01/12/2022 – 15/12/2022: Visiting Researcher, Dept. of Mathematics, Brandeis University, Waltham, Massachusetts, USA
• 01/10/2022 – 30/10/2022: Visiting Researcher, Inria Sophia Antipolis Méditerranée Research Centre, Valbonne, France
• 01/02/2019 – 30/04/2019: Visiting Researcher, Dept. of Applied Mathematics, Technical University of Denmark, Denmark

• 10/2014 – 02/2018: PhD in Applied Mathematics, Max Planck Institute for Mathematics in the Sciences, Leipzig
• 10/2013 – 09/2014: Studies in Applied Mathematics, University of Leipzig, Germany
• 10/2011 – 05/2013: M.Sc. in Theoretical Physics, University of Buea, Cameroon

• 10/2014 – 02/2018: IMPRS Scholarship, Max Planck Institute for Mathematics in the Sciences, Leipzig
• 04/2021 – 03/2023: DFG (German Research Foundation) Individual Research Grant

• 04/2020 – present: Teaching Assistant, Department Mathematik, Friedrich-Alexander-Universität Erlangen-Nürnberg
  • Exercise class: Mathematics for Engineers C4: Stochastics, summer semester 2020
• 02/2019 – 04/2019: Visiting Lecturer, Department of Applied Mathematics, Technical University of Denmark
  • Lecture: Dynamical Systems 2 (M.Sc. course)
  • Exercise class: Dynamical Systems 2
• 10/2016 – 10/2017: Lecturer, Max Planck Institute for Mathematics in the Sciences
  • Seminar series: 5 Lectures on Stochastic Neuronal Dynamics
• 10/2012 – 06/2013: Teaching Assistant, Department of Physics, University of Buea
  • Exercise class: PHY 202 Classical Mechanics 1 (B.Sc. course)
  • Exercise class: PHY 301 Classical Mechanics 2 (B.Sc. course)
  • Exercise class: PHY 207 Mathematical Methods for Physics 1 (B.Sc. course)
  • Exercise class: PHY 306 Mathematical Methods for Physics 2 (B.Sc. course)

• 10/2022: International Conference on Complex Systems, Palma de Mallorca, Spain
• 09/2022: Workshop on Control of Self-Organizing Nonlinear Systems, Wittenberg, Germany
• 06/2022: 7th International Conference on Random Dynamical Systems (online), Hanoi, Vietnam
• 05/2022: Nonlinear Dynamics Seminar, Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
• 09/2021: International Conference on Stochastic Resonance, Perugia, Italy
• 05/2021: SIAM Conference on Applications of Dynamical Systems (online), Portland, USA
• 11/2020: Mini-workshop on Neuronal Dynamics, University of Erlangen-Nürnberg, Germany
• 08/2020: XL. Dynamics Days Europe (online), Nice, France
• 02/2020: Oberseminar: Dynamics, Department of Mathematics, Technical University of Munich, Germany
• 09/2019: 15th Seminar on Stochastic and Collective Effects in Neural Systems, University of Granada, Spain
• 07/2019: Int. Workshop on Oscillations, Transients and Fluctuations in Complex Networks, University of Copenhagen
• 09/2018: Bernstein Conference on Computational Neuroscience, Technical University of Berlin, Germany
• 07/2018: 27th Annual Meeting, Organization for Computational Neurosciences, Seattle, USA
• 05/2018: Seminar: Dynamics and Control of Complex Networks, Inst. of Theoretical Physics, Technical University of Berlin
• 06/2018: 4th International Conference on Mathematical Neuroscience, Juan-les-Pins, France
• 09/2017: Bernstein Conference on Computational Neuroscience, University of Göttingen, Germany
• 05/2017: SIAM Conference on Applications of Dynamical Systems, Snowbird, Utah, USA
• 11/2016: 3rd Dresden-Leipzig Dynamics Day, Technical University of Dresden, Dresden, Germany
• 06/2015: Int. Workshop: Dynamics of Multi-Level Systems, Max Planck Institute for Physics of Complex Systems, Dresden

My research interest is interdisciplinary and lies in the area between applied mathematics, theoretical physics, neurobiology, and computational neuroscience, tapping from each to uncover the origins of the brain’s computational power and use the uncovered knowledge to optimize machine learning algorithms.

• Stochastic nonlinear dynamics and statistical physics
• Geometric singular perturbation theory
• Theoretical and computational neuroscience
• Mathematical modeling of neural systems
• Chaos- and noise-induced resonance and synchronization in adaptive neural networks
• Spatio-temporal patterns in neural field equations
• Biological neural networks and Hebbian learning algorithms
• Computation through neural population dynamics
• Reservoir computing, echo-state networks, and liquid-state machines
• Artificial neural networks and deep learning algorithms
• Physics-informed artificial neural networks
• Numerical simulations and data analysis
• In particular, the interfaces between the above fields
• … with enthusiastic collaborations with experimental neuroscientists

• Diversity-induced decoherence.
  Marius E. Yamakou, Els Heinsalu, Marco Patriarca, Stefano Scialla
  Physical Review E 106, L032401 (2022)
• Optimal resonances in multiplex neural networks driven by an STDP learning rule.
  Marius E. Yamakou, Tat D. Tran, Jürgen Jost
  Frontiers in Physics 10, 909365 (2022)
• Lévy noise-induced self-induced stochastic resonance in a memristive neuron.
  Marius E. Yamakou, Tat D. Tran
  Nonlinear Dynamics 107, 2847-2865 (2022)
• Control of noise-induced coherent oscillations in three-neuron motifs.
  Florian Bönsel, Claus Metzner, Patrick Krauss, Marius E. Yamakou
  Cognitive Neurodynamics 16, 2847–2865 (2022)
• Chaotic synchronization of memristive neurons: Lyapunov function versus Hamilton function.
  Marius E. Yamakou
  Nonlinear Dynamics 101, 487-500 (2020)
• Optimal self-induced stochastic resonance in multiplex neural networks: electrical versus chemical synapses.
  Marius E. Yamakou, Poul G. Hjorth, Erik A. Martens
  Frontiers in Computational Neuroscience 14, 62 (2020)
• The stochastic FitzHugh-Nagumo neuron model in the excitable regime embeds a leaky integrate-and-fire model.
  Marius E. Yamakou, Tat D. Tran, Luu H. Duc, Jürgen Jost
  Journal of Mathematical Biology 79, 509-532 (2019)
• Control of coherence resonance by self-induced stochastic resonance in a multiplex neural network.
  Marius E. Yamakou, Jürgen Jost
  Physical Review E 100, 022313 (2019)
• Weak-noise-induced transitions with inhibition and modulation of neural oscillations.
  Marius E. Yamakou, Jürgen Jost
  Biological Cybernetics 112, 445-463 (2018)
• Coherent neural oscillations induced by weak synaptic noise.
  Marius E. Yamakou, Jürgen Jost
  Nonlinear Dynamics 93, 2121-2144 (2018)
• A simple parameter can switch between different weak-noise-induced phenomena in a simple neuron model.
  Marius E. Yamakou, Jürgen Jost
  EPL (Europhysics Letters) 120, 18002 (2017)
• Ratcheting and energetic aspects of synchronization in coupled bursting neurons.
  Marius E. Yamakou, E. Maeva Inack, F. M. Kakmeni Moukam
  Nonlinear Dynamics 83, 541-554 (2016)
• Localized nonlinear excitations in diffusive Hindmarsh-Rose neural network.
  F. M. Kakmeni Moukam, E. Maeva Inack, Marius E. Yamakou
  Physical Review E 89, 052919 (2014)
• Coherence resonance and stochastic synchronization in a small-world neural network: An interplay in the presence of STDP.
  Marius E. Yamakou, Estelle M. Inack
  arXiv Submitted (2022)
• Dynamics of neural fields with exponential temporal kernel.
  Elham Shamsara, Marius E. Yamakou, Fatihcan M. Atay, Jürgen Jost
  arXiv Submitted (2022)
• Combined effect of STDP and homeostatic structural plasticity on coherence resonance in adaptive neural networks.
  Marius E. Yamakou, Christian Kuehn
  arXiv in Preparation (2022)