Marius Yamakou
Dr. Marius Yamakou
Faculty of Sciences
Department of Data Science (DDS)
Assistance
Office hours: By appointment via email.
- 04/2021 – present: Researcher and Lecturer, Department of Data Science, FAU Erlangen-Nürnberg
- 10/2024 – 03/2025: Interim Professor, Department of Data Science, FAU Erlangen-Nürnberg
- 11/2019 – 03/2021: Postdoctoral Researcher, Department of Mathematics, FAU Erlangen-Nürnberg
- 02/2018 – 10/2019: Postdoctoral Researcher, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- 24/03/2025 – 05/04/2025: INRIA Research Centre, Université de Montpellier, France
- 18/08/2024 – 27/08/2024: Centre for Mathematical Sciences, Lund University, Sweden
- 28/05/2023 – 03/06/2023: Centre for Mathematical Sciences, Lund University, Sweden
- 12/02/2023 – 23/02/2023: Perimeter Institute for Theoretical Physics, Waterloo, Canada
- 05/12/2022 – 18/12/2022: Department of Mathematics, Brandeis University, Massachusetts, USA
- 01/10/2022 – 30/10/2022: INRIA Research Centre, Université Côte d’Azur, France
- 01/02/2019 – 30/04/2019: Department of Applied Mathematics, Technical University of Denmark, Denmark
- 11/2019 – present: Habilitation Candidate in Applied Mathematics, Department of Mathematics, FAU Erlangen-Nürnberg
- Thesis: Nonlinear and stochastic dynamics in neural networks: An analytical and computational approach
- 10/2014 – 02/2018: Ph.D. in Mathematics, Max Planck Institute for Mathematics in the Sciences, Leipzig
- Thesis: Weak stochastic perturbations of slow-fast dynamical systems
- 04/2025 – 06/2025: Funding for scientific outreach activity in Data Science, ∼ € 1.6K
- 04/2021 – 03/2023: DFG Research Grant as sole Principal Investigator (Project No. 456989199), ∼ € 200K
- 10/2014 – 02/2018: IMPRS Scholarship, Max Planck Institute for Mathematics in the Sciences, Leipzig
I develop and unite tools from applied mathematics, theoretical physics, and machine learning to model, infer, and control complex dynamical systems—ranging from neuronal networks in the brain to emergent behavior in physical and biological systems. By merging modern data-driven methods with the rigorous framework of dynamical systems theory and non-equilibrium statistical physics, my research uncovers the underlying governing equations and devises principled strategies for their optimal control.
- Dynamical system theory and nonlinear dynamics
- Statistical physics and stochastic analysis
- Geometric singular perturbation theory
- Data-driven methods and inference in dynamical systems
- Bayesian methodology for high-dimensional and complex data
- Mathematical neuroscience, neuronal and brain dynamics
- Chaos- and noise-induced resonance and synchronization phenomena
- Self-organization and critical dynamics in adaptive neural networks
- Spiking neural networks (SNN) and their applications
- Convolutional neural networks (CNN), recurrent neural networks (RNN)
- Port-Hamiltonian neural networks (pHNN)
- Physics-informed neural network (PINN) and constrained optimization
- Neuroscience-inspired machine learning: liquid-state machines & echo-state networks
- In particular, the interfaces between the above fields!
- Effect of diversity distribution symmetry on global oscillations of networks of excitable units
Stefano Scialla, Marco Patriaca, Els Heinsalu, Marius E. Yamakou, Julyan Cartwright
https://arxiv.org/abs/2507.09804, Submitted (2025) - Dynamical equivalence between resonant translocation of a polymer chain and diversity-induced resonance.
Marco Patriaca, Stefano Scialla, Els Heinsalu, Marius E. Yamakou, Julyan Cartwright
Chaos: An Interdisciplinary Journal of Nonlinear Science 35, 073115 (2025) - Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: A dynamical system versus machine learning approach.
Jan Kobiolka, Jens Habermann, Marius E. Yamakou
Nonlinear Dynamics 113, 10989-11008 (2024) - Inverse stochastic resonance in adaptive small-world neural network.
Marius E. Yamakou, Jinjie Zhu, Erik A. Marten
Chaos: An Interdisciplinary Journal of Nonlinear Science 34, 113119 (2024) - Dynamics of neural fields with exponential temporal kernel
Elham Shamsara, Marius E. Yamakou, Fatihcan M. Atay, Jürgen Jost
Theory in Biosciences 143, 107-122 (2024) - Quantifying and maximizing the information flux in recurrent neural networks.
Claus Metzer, Marius E. Yamakou, Dennis Voelkl, Achim Schilling, Patrick Krauss,
Neural Computation 36, 351-384 (2024) - Synchronization in STDP-driven memristive neural networks with time-varying topology.
Marius E. Yamakou, Serafim Rodrigues, Mathieu Desroches
Journal of Biological Physics 49, 483-507 (2023) - Self-induced-stochastic-resonance breathing chimera.
Jinjie Zhu, Marius E. Yamakou
Physical Review E 108, L022204 (2023) - Combined effect of STDP and homeostatic structural plasticity on coherence resonance.
Marius E. Yamakou, Christian Kuehn
Physical Review E 107, 044302 (2023) - Coherence resonance and synchronization in a small-world neural network: An interplay in the presence of STDP.
Marius E. Yamakou, Estelle M. Inack
Nonlinear Dynamics 111, 7789-7805 (2023) - 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 (2021) - Control of noise-induced coherent oscillations in three-neuron motifs
Florian Bönsel, Claus Metzner, Patrick Krauss, Marius E. Yamakou
Cognitive Neurodynamics 16, 2847-2865 (2021) - 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 (2015) - 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)
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- Lecture and exercise classes: Theory of Neural Dynamics and Applications to Machine Learning, SS 25
- Lecture: Selected Topics in Mathematics of Learning, WS 24/25
- Invited Lecture: Machine learning and physics-informed neural networks, FAU Research Training Group FRASCAL (fracture across scales), WS 25
- Lecture and exercise classes: Dynamical System Theory for Data Scientists, WS 24/25
- Lecture and exercise classes: Theory of Neural Dynamics and Applications to Machine Learning, SS 24
- Lecture and exercise classes: Dynamical System Theory for Data Scientists, WS 23/24
- Lecture and exercise classes: Theory of Neural Dynamics and Applications to Machine Learning, SS 23
- Exercise classes: Mathematics for Engineers: Stochastics, SS 20
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- Lecture and exercise classes: Dynamical Systems 2 (M.Sc. course)
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- Seminar lecture series: 5 lectures on Stochastic Neural Dynamics
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- Exercise classes: PHY 202: Newtonian Mechanics
- Exercise classes: PHY 207: Mathematical Methods for Physics 1
- Exercise classes: PHY 301: Analytical Mechanics
- Exercise classes: PHY 306: Mathematical Methods for Physics 2
I’m open to brainstorming master’s thesis topics that could grow into a publishable, high-quality journal article. I’m also happy to supervise master’s theses conducted in collaboration with industry partners.
- 07/2024: Jay Pramod Asodariya, Motion estimation and magnification using swin V2 image transformer and deep convolutional neural networks.
- 05/2024: Jan Kobiolka, Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: A dynamical system versus machine learning approach, Nonlinear Dynamics 113, 10989-11008 (2024)
- 04/2021: Florian Bönsel, Control of noise-induced coherent oscillations in three-neuron motifs, Cognitive Neurodynamics 16, 2847-2865 (2021)
- 2025 – present: Alina Schlabritz, Optimization of noise-induced resonances in higher-order neural networks.
- 2025 – present: Divyesh Savaliya, A self-induced stochastic resonance problem: A physics-informed neural network approach
- 2025 – present: Behnam Babaeian, Asymptotic stability of a chaotic synchronization manifold using a rigorous stability proof and port-Hamiltonian physics-informed neural networks
- 06/2025: International Conference on Mathematical Neuroscience, Centre de Recerca Matemàtica, Barcelona, Spain
- 04/2025: Mathematical Neuroscience Seminar, INRIA Research Center, University of Montpellier, France
- 10/2024: Biointerfaces Seminar, Laboratory of Biointerfaces, Dept. of Chemistry and Pharmacy, FAU, Erlangen
- 08/2024: International Workshop on Network Dynamics, Centre for Mathematical Sciences, Lund University, Sweden
- 07/2024: International Conference – XLIV Dynamics Days Europe, Bremen, Germany
- 06/2024: International Conference on Mathematical Neuroscience, University College Dublin, Ireland
- 11/2023: Workshop: Dynamics in Coupled Networks, Weierstrass Institute for Applied Analysis & Stochastics, Berlin
- 10/2023: Computational Neuroscience Seminar: University College London (UCL), London, United Kingdom
- 07/2023: 32nd Annual Meeting, Organization for Computational Neurosciences, Leipzig, Germany
- 06/2023: Dynamical Systems Seminar, Centre for Mathematical Sciences, Lund University, Sweden
- 05/2023: Seminar of Statistical Learning and Dynamical Systems, ScaDS.AI Institute, Uni. of Leipzig, Germany
- 05/2023: SIAM Conference on Applications of Dynamical Systems, Portland, Oregon, USA
- 03/2023: Dynamical Systems Seminar, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- 02/2023: Dynamical systems seminar, Department of Applied Mathematics, University of Waterloo, Canada
- 02/2023: 14th Conference on Dynamical Systems Applied to Biology and Natural Sciences, Bilbao, Spain
- 12/2023: Dynamical Systems in Neuroscience seminar, Brandeis University, Waltham, Massachusetts, USA
- 11/2022: International Conference Control of Self-Organizing Nonlinear Systems, Potsdam, Germany
- 10/2022: 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, 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, Portland, USA
- 11/2020: Mini-workshop on Neuronal Dynamics, University of Erlangen-Nürnberg, Germany
- 08/2020: XL. Dynamics Days Europe, 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: 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
- 03/2018: Deutsche Physikalische Gesellschaft (DPG) Spring Meeting, Berlin, Germany
- 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: Workshop: Dynamics of Multi-Level Systems, Max Planck Institute for Physics of Complex Systems, Dresden
- 04/2025: Co-organizer of the Conference on Women in Data Science, FAU, Erlangen
- 04/2024: Co-organizer of the Conference on Women in Data Science, FAU, Erlangen
- 04/2023: Co-organizer of the Conference on Women in Data Science, FAU, Erlangen
- 03/2020 – 07/2021: Organizer of the Online Seminar Series on Dynamics and Control (93 talks), FAU
- 11/2020: Organizer of the Mini-workshop on Neuronal Dynamics (3 talks), FAU, Erlangen
- 11/2020: Organizer of the Mini-workshop on Robots Learning, Optimization & Control (4 talks), FAU, Erlangen
- 10/2020: Organizer of the Mini-workshop on Hyperbolic Problems (5 talks), FAU, Erlangen
- 03/2025: Der Tag der Mathematik, Department Mathematik, FAU, Erlangen
- 06/2016: Science Communicator at the Lange Nacht der Wissenschaften 2016, Inselstrasse 22, Leipzig
- AIP Advances
- Applied Physics and Engineering
- Applied Physics Letters
- Applications of Mathematics
- Brain Sciences
- Chaos: An Interdisciplinary Journal of Nonlinear Science
- Chaos, Solitons and Fractals: The Journal of Nonlinear Science, Nonequilibrium and Complex Phenomena
- Cognitive Neurodynamics
- Communications in Nonlinear Science and Numerical Simulations
- Discrete Dynamics in Nature and Society
- European Physical Journal Plus
- European Physical Journal Special Topics
- Fluctuation and Noise Letters
- Frontiers in Computational Neuroscience
- Journal of Computational and Nonlinear Dynamics
- Journal of Mathematical Biology
- Journal of Sound and Vibration
- International Journal of Bifurcation and Chaos
- Mathematics (ISSN 2227-7390)
- Mathematical Methods in the Applied Sciences
- Neural Processing Letters
- Neural Networks
- Nonlinear Dynamics: An International Journal of Nonlinear Dynamics and Chaos
- Physica A: Statistical Mechanics and its Applications
- Physica D: Nonlinear Phenomena
- Physical Review E
- Physics Letters A
- Reviews of Modern Physics