Learning latent low-dimensional dynamics from neural population responses: a stochastic control approach
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portfolio
Multiple adaptation mechanisms account for the emergence of prediction error responses in V1
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. 
Shaping low-rank recurrent neural networks with biological learning rules
Biologically-plausible training of low-rank RNNs - with Pablo Crespo, Dimitra Maoutsa, Matt Getz 
Plasticity-driven circuit self-organization on spiking stabilized supralinear networks
How different forms of spike-timing dependent plasticity affect the functional properties of spiking SSNs - with Raul Adell Segarra, Dylan Festa, Dimitra Maoutsa 
Revealing latent stochastic dynamics from neuronal spiking patterns
Inference of latent dynamics through stochastic control - with myself 
publications
Inferring network connectivity from event timing patterns
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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Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation
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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Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
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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Deterministic particle flows for constraining SDEs
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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Deterministic particle flows for constraining stochastic nonlinear systems
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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Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems
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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Geometric constraints improve inference of sparsely observed stochastic dynamics
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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Plasticity-driven circuit self-organization on spiking stabilized supralinear networks
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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Identifying plasticity mechanisms underlying experience-driven adaptation in cortical circuits
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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talks
Revealing network physical interactions from event timing patterns: A model-free approach
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One hour long talk at the Workshop Stochastic dynamics on large networks: Prediction and inference
Deterministic particle solutions to Fokker-Planck equations
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Twenty minutes talk at the CRC 1294 virtual event organised by the Institute of Mathematics at the University of Potsdam.
Geometric methods for inference of nonlinear systems
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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.
Learning latent low-dimensional dynamics from neural population responses: a stochastic control approach
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One hour long seminar at the Center for Computational Neuroscience, Flatiron institute.
Learning latent low-dimensional dynamics from neural population responses: a stochastic control approach
Published:
Fourty-five minutes seminar talk at the Institute for Adaptive and Neural Computation, University of Edinburgh.
teaching
Network Dynamics & Complex Systems – Theoretical and Computational Tools (M.Sc.)
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)
Introduction to Data Science (M.Sc./Ph.D.)
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
Introduction to Computational Neuroscience – Tutorials (M.Sc.)
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.
Introduction to Computational Neuroscience – Research Projects (M.Sc.)
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.
Mathematical Models of Neural Systems and Cognitive Functions – Tutorials (M.Sc.)
Tutoring, Ludwig Maximilian University of Munich, Graduate School of Systemic Neurosciences, 2024
Tutorials on spike-timing dependent plasticity, rate-based plasticity and network dynamics