My work bridges mathematical neuroscience and machine learning. Leveraging dynamical systems theory, stochastic analysis, and physics-informed machine learning, I develop data-driven models of neurons and large-scale brain activity to better understand pathological dynamics in disorders such as epilepsy and Parkinson’s disease. Drawing inspiration from neural computation, I design neuroscience-inspired machine-learning algorithms that deepen our fundamental insight into brain function while powering new algorithms, neurotechnologies, and medical applications.
Research projects
Dynamical systems theory
Stochastic analysis
Statistical physics
Mathematical and computational neuroscience
Data-driven methods for neuronal and brain dynamics
Mathematical and data-driven modelling of epilepsy and Parkinson’s disease
Neuroscience-inspired machine learning (e.g., liquid-state machines)
Physics-informed machine learning and constrained optimisation
Recent publications
2025
Patriarca, M., Scialla, S., Heinsalu, E., Yamakou, M., & Cartwright, J.H. (2025). Dynamical equivalence between resonant translocation of a polymer chain and diversity-induced resonance . Chaos , 35 (7). https://doi.org/10.1063/5.0262633
Scialla, S., Patriarca, M., Heinsalu, E., Yamakou, M., & Cartwright, J.H. (2025). Effect of diversity distribution symmetry on global oscillations of networks of excitable units . Physical Review E , 112 (5). https://doi.org/10.1103/lvb3-dc11
2024
Kobiolka, J., Habermann, J., & Yamakou, M. (2024). Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: a dynamical system versus machine learning approach . Nonlinear Dynamics . https://doi.org/10.1007/s11071-024-10821-6
Metzner, C., Yamakou, M., Voelkl, D., Schilling, A., & Krauß, P. (2024). Quantifying and Maximizing the Information Flux in Recurrent Neural Networks . Neural Computation , 36 (3), 351-384. https://doi.org/10.1162/neco_a_01651
Shamsara, E., Yamakou, M., Atay, F.M., & Jost, J. (2024). Dynamics of neural fields with exponential temporal kernel . Theory in Biosciences . https://doi.org/10.1007/s12064-024-00414-7
Yamakou, M., Zhu, J., & Martens, E.A. (2024). Inverse stochastic resonance in adaptive small-world neural networks . Chaos , 34 (11). https://doi.org/10.1063/5.0225760
2023
Yamakou, M., Desroches, M., & Rodrigues, S. (2023). Synchronization in STDP-driven memristive neural networks with time-varying topology . Journal of Biological Physics . https://doi.org/10.1007/s10867-023-09642-2
Yamakou, M., & Inack, E.M. (2023). Coherence resonance and stochastic synchronization in a small-world neural network: an interplay in the presence of spike-timing-dependent plasticity . Nonlinear Dynamics . https://doi.org/10.1007/s11071-023-08238-8
Yamakou, M., & Kuehn, C. (2023). Combined effects of spike-timing-dependent plasticity and homeostatic structural plasticity on coherence resonance . Physical Review E , 107 (4). https://doi.org/10.1103/PhysRevE.107.044302
Zhu, J., & Yamakou, M. (2023). Self-induced-stochastic-resonance breathing chimeras . Physical Review E , 108 (2). https://doi.org/10.1103/PhysRevE.108.L022204
2022
Yamakou, M., Heinsalu, E., Patriarca, M., & Scialla, S. (2022). Diversity-induced decoherence . Physical Review E , 106 (3). https://doi.org/10.1103/PhysRevE.106.L032401
Yamakou, M., Tran, T.D., & Jost, J. (2022). Optimal Resonances in Multiplex Neural Networks Driven by an STDP Learning Rule . Frontiers in Physics , 10 . https://doi.org/10.3389/fphy.2022.909365
2021
Bönsel, F., Krauß, P., Metzner, C., & Yamakou, M. (2021). Control of noise-induced coherent oscillations in three-neuron motifs . Cognitive Neurodynamics , 16 , 941-960. https://doi.org/10.1007/s11571-021-09770-2
Yamakou, M., & Tran, T.D. (2021). Lévy noise-induced self-induced stochastic resonance in a memristive neuron . Nonlinear Dynamics . https://doi.org/10.1007/s11071-021-07088-6
2020
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My work bridges mathematical neuroscience and machine learning. Leveraging dynamical systems theory, stochastic analysis, and physics-informed machine learning, I develop data-driven models of neurons and large-scale brain activity to better understand pathological dynamics in disorders such as epilepsy and Parkinson’s disease. Drawing inspiration from neural computation, I design neuroscience-inspired machine-learning algorithms that deepen our fundamental insight into brain function while powering new algorithms, neurotechnologies, and medical applications.
Research projects
No projects found.
2025
2024
2023
2022
2021
2020
Related Research Fields:
Contact: