We identify possible plasticity mechanisms that explain the adaptation of responses to repeated stimulus presentations and the emergence of prediction error responses in a recurrent circuit model with multiple interneuron sub-types. 
Biologically-plausible training of low-rank RNNs - with Pablo Crespo, Dimitra Maoutsa, Matt Getz 
How different forms of spike-timing dependent plasticity affect the functional properties of spiking SSNs - with Raul Adell Segarra, Dylan Festa, Dimitra Maoutsa 
Inference of latent dynamics through stochastic control - with myself 
Published in Physical Review Letters, 2018
Inference method for identifying synaptic interactions from spiking patterns based on a mapping of the spiking activity onto event spaces, where the connectivity can be estimated locally by approximating the interspike interval generating function.
Recommended citation: Casadiego*, J; Maoutsa*, D; Timme, M. (2018). "Inferring network connectivity from event timing patterns." Physical Review Letters. 121.5 (2018): 054101.
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Published in Entropy, 2020
Introduced an interacting particle system with purely deterministic dynamics that provides accurate Fokker–Planck solutions for diffusive systems.
Recommended citation: Maoutsa, D; Reich, S; Opper, M. (2020). "Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation." Entropy. 22.8 (2020): 802.
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Published in Neuron, 2021
Brainhack community paper.
Recommended citation: Rémi Gau*, Stephanie Noble*, Katja Heuer*, Katherine L Bottenhorn*, Isil P Bilgin*, Yu-Fang Yang*, Julia M Huntenburg*, Johanna MM Bayer*, Richard AI Bethlehem*, et al. (The Brainhack Community). (2021). "Brainhack: Developing a culture of open, inclusive, community-driven neuroscience." Neuron. 1(3).
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Published in NeurIPS workshop Machine Learning for the physical sciences, 2021
Non-iterative stochastic control framework based on deterministic particle dynamics.
Recommended citation: Maoutsa, D; Opper, M. (2021). "Deterministic particle flows for constraining SDEs." NeurIPS workshop Machine Learning for the physical sciences. 1(3).
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Published in Physical Review Research, 2022
Recommended citation: Maoutsa, D; Opper, M. (2022). "Deterministic particle flows for constraining stochastic nonlinear systems." Physical Review Research. 4.4 (2022): 043035.
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Published in NeurIPS workshop Machine Learning for the physical sciences, 2022
Recommended citation: Maoutsa, D. (2022). "Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems." NeurIPS workshop Machine Learning for the physical sciences. 4.4 (2022): 043035.
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Published in ICLR workshop Physics for Machine Learning, 2023
Recommended citation: Maoutsa, D. (2022). "Geometric constraints improve inference of sparsely observed stochastic dynamics." ICLR workshop Physics for Machine Learning.
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Published in (in preparation), 2024
Recommended citation: Adell Segarra,R; Festa, D; Maoutsa, D; Gjorgjieva, J. (in preparation). "Plasticity-driven circuit self-organization on spiking stabilized supralinear networks." (in preparation).
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Published in (in preparation), 2024
Recommended citation: Maoutsa, D; Gjorgjieva, J. (in preparation). "Identifying plasticity mechanisms underlying experience-driven adaptation in cortical circuits." (in preparation). .
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Published:
One hour long talk at the Workshop Stochastic dynamics on large networks: Prediction and inference
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Twenty minutes talk at the CRC 1294 virtual event organised by the Institute of Mathematics at the University of Potsdam.
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Twenty minutes seminar talk at the group meeting of the Gjiorgjieva lab at the School of Life Sciences at the Technical University of Munich.
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One hour long seminar at the Center for Computational Neuroscience, Flatiron institute.
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Fourty-five minutes seminar talk at the Institute for Adaptive and Neural Computation, University of Edinburgh.
teaching, Third Institute of Physics, Georg - August University of Göttingen, 2016
Weekly lecture and tutorial on Graph Theory, Random Networks, Network Dynamics, and Dynamics on Networks for Master’s physics students (4h/week)
teaching, Göttingen Graduate Center for Neurosciences, Biophysics, and Molecular Biosciences (GGNB), Georg - August University of Göttingen, 2017
One week intensive graduate course on Data Science and Machine Learning with Python for Göttingen Graduate Center for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) graduate students
Tutoring, School of Life Sciences, Technical University of Munich, 2023
Tutorials focusing on practical implementation in Python of concepts covered during the lecture Introduction to Computational Neuroscience. Topics included neural coding, rate neuronal models, spike-timing and rate-based plasticity, dimensionality reduction, and reinforcement learning.
Research project tutoring, School of Life Sciences, Technical University of Munich, 2023
Solely directed and mentored 6 groups (2-3 students each) in executing practical research projects that served as their final evaluation for the lecture Introduction to Computational Neuroscience.
Tutoring, Ludwig Maximilian University of Munich, Graduate School of Systemic Neurosciences, 2024
Tutorials on spike-timing dependent plasticity, rate-based plasticity and network dynamics