# Marius Yamakou

### Dr. Marius Yamakou

Office hours: Wednesdays, 11:30 am – 1:00 pm, Room: 02.343

## Curriculum vitae

- 04/2021 – present:
**Researcher and Lecturer**, Department 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

## Invited research visits

- 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 Sophia Antipolis Méditerranée Research Centre, Valbonne, France
- 01/02/2019 – 30/04/2019: Department of Applied Mathematics, Technical University of Denmark, Denmark

## Education

- 05/2024:
**Habilitation Candidate****in Applied Mathematics**, University of Erlangen-Nürnberg - 10/2014 – 02/2018:
**Ph.D. in Applied Mathematics**, Max Planck Institute for Mathematics in the Sciences, Leipzig - 10/2013 – 09/2014: Studies in Mathematics, University of Leipzig
- 10/2011 – 06/2013:
**M.Sc. in Theoretical Physics**, University of Buea

## Grants and funding

- 10/2014 – 02/2018: IMPRS Scholarship, Max Planck Institute for Mathematics in the Sciences, Leipzig
- 04/2021 – 03/2023: DFG Research Grant as Principal Investigator (Project No. 456989199), ∼ € 200K

## Teaching and tutorials

- 04/2020 – present: Departments of Mathematics and Data Science, University of Erlangen-Nürnberg
- 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

- 02/2019 – 04/2019: Department of Applied Mathematics, Technical University of Denmark
- Lecture and exercise classes: Dynamical Systems 2 (M.Sc. course)

- 10/2016 – 10/2017: Max Planck Institute for Mathematics in the Sciences, Leipzig
- Seminar lecture series: 5 lectures on Stochastic Neural Dynamics

- 10/2012 – 06/2013: Department of Physics, University of Buea
- 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

## Students and thesis supervision

I am pleased to discuss possible bachelor’s & master’s thesis topics at the intersection of dynamical systems theory, machine learning, computational neuroscience, and data science.

## Master thesis

- 2024 – present: Behnam Babaeian, Dynamics and data-driven forecasting of energy consumption during synchronization in neural networks with switchable memory
- 2024 – present: Pooya Khalaji, Forecasting coherence resonance in Hodgkin-Huxley neural networks
- 03/2024 – present: Jay Asodariya, Motion estimation and magnification using swin V2 image transformer and deep convolutional neural networks
- 10/2023 – 05/2024: Jan Kobiolka, Reduced-order adaptive synchronization in a chaotic neural network with parameter uncertainty: A dynamical system vs. machine learning approach
- 04/2020 – 04/2021: Florian Bönsel, Control of noise-induced coherent oscillations in three-neuron motifs
- 03/2016 – 02/2017: Gregor Schuldt, Analyse des Morris-Lecar-Neuronenmodells mit stochastischen Störungen

## Bachelor thesis

## Talks at conferences, workshops, and seminars

- 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

## Research interests and methods

My research interest is interdisciplinary and centered at the intersection of applied mathematics, theoretical physics, complex systems, computational neuroscience, and machine learning. My approach involves leveraging insights from these fields to unravel the fundamental principles underlying the computational power of biological neurons and the brain. I aim to use these principles to develop and optimize neuroscience-inspired machine learning algorithms and computing.

- Dynamical system theory and nonlinear dynamics
- Statistical physics and stochastic dynamics
- Geometric singular perturbation theory
- Complex systems theory
- Adaptive network dynamics
- Chaos- and noise-induced resonance and synchronization phenomena
- Self-organization and critical dynamics in complex adaptive systems
- Theory of neuronal dynamics and mathematical neuroscience
- Numerical simulations and data analysis
- Computation through neural population dynamics
- Spiking neural networks and their applications
- Data science in neurodynamics
- Neuroscience-inspired machine learning: liquid-state machines & echo-state networks
- In particular, the interfaces between the above fields!

## Publications (peer-reviewed) and preprints

*Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: A dynamical system vs. machine learning approach*

Jan Kobiolka, Jens Habermann,**Marius E. Yamakou***(In preparation)**Inverse stochastic resonance in adaptive small-world neural networks*

**Marius E. Yamakou**, Jinjie Zhu, Erik A. Martens

arXiv: 2407.03151, submitted (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 Metzner,**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)