A dynamical systems theory of reward-modulated learning in linear recurrent networks
Maoutsa, D.(2026). "A dynamical systems theory of reward-modulated learning in linear recurrent networks." Neural Information Processing Systems.
CV
Training
- PostDoc on Theoretical & Computational Neuroscience, School of Life Sciences - Technical University of Munich, 2024
- Ph.D. on Electrical Engineering & Computer Science (Stochastic Dynamics and Machine Learning), Technical University of Berlin, 2023 (in collaboration with Institute of Mathematics of University of Potsdam)
- Visiting Ph.D. researcher on Mathematics (Stochastic Analysis), University of Potsdam, 2021
- Research Assistant in Theoretical Physics (Nonlinear & Network Dynamics), Max Planck Institute for Dynamics and Self-Organisation, 2017
- M.Sc. in Computer Science & Computational Neuroscience, Georg August University of Goettingen & Max Planck Institute for Dynamics and Self-Organisation, 2016
- Diploma in Electrical and Computer Engineering, National Technical University of Athens, 2013
Publications
How reward and experience shape neural population dynamics in mouse visual cortex
Madrid-Carvajal, J. et. al, (2026). "How reward and experience shape neural population dynamics in mouse visual cortex." Neuromatch Impact Scholars Program 2025. (2026)
From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints
Maoutsa, D., (2026). "From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints." Fourty-third International Conference on Machine Learning (ICML 2026)
Learning to learn dynamical associations with reward-gated local plasticity
Maoutsa, D.(2026). "Learning to learn dynamical associations with reward-gated local plasticity." International Conference for Learning Representations-New Frontiers in Associative Memories workshop.
Flow curvature explains failed SDE drift estimation under sparse sampling
Maoutsa, D.(2026). "Flow curvature explains failed SDE drift estimation under sparse sampling." International Conference for Learning Representations-Geometry-grounded Representation Learning and Generative Modeling workshop.
Discovering neo-Hebbian plasticity rules for reward-driven training of RNNs
Maoutsa, D.(2025). "Discovering neo-Hebbian plasticity rules for reward-driven training of RNNs." NeurIPS-WiML workshop.
Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback
Maoutsa, D.(2025). "Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback." NeurIPS-NeurReps workshop.
Geometric constraints improve inference of sparsely observed stochastic dynamics
Maoutsa, D. (2022). "Geometric constraints improve inference of sparsely observed stochastic dynamics." ICLR workshop Physics for Machine Learning.
Deterministic particle flows for stochastic nonlinear systems: Simulation, Control, and Inference
Maoutsa, D. (2023). "Deterministic particle flows for stochastic nonlinear systems: Simulation, Control, and Inference." Technical University of Berlin. 1(3).
Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems
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.
Deterministic particle flows for constraining stochastic nonlinear systems
Maoutsa, D; Opper, M. (2022). "Deterministic particle flows for constraining stochastic nonlinear systems." Physical Review Research. 4.4 (2022): 043035.
Deterministic particle flows for constraining SDEs
Maoutsa, D; Opper, M. (2021). "Deterministic particle flows for constraining SDEs." NeurIPS workshop Machine Learning for the physical sciences. 1(3).
Brainhack: Developing a culture of open, inclusive, community-driven neuroscience
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).
Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation
Maoutsa, D; Reich, S; Opper, M. (2020). "Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation." Entropy. 22.8 (2020): 802.
Inferring network connectivity from event timing patterns
Casadiego*, J; Maoutsa*, D; Timme, M. (2018). "Inferring network connectivity from event timing patterns." Physical Review Letters. 121.5 (2018): 054101.
Talks
Learning to learn with reward-gated local plasticity
Contirbuted talk at BrainNet Workshop, KTH, Stockholm, Sweden
Identifying plasticity mechanisms underlying context-dependent neural responses in cortical circuits
Contirbuted talk at Junior Theoretical Neuroscientist’s Workshop, Flatiron Institute, New York, USA
Evolving dynamical landscape of RNNs during continual learning
Invited talk at Marine Biological Laboratory, University of Chicago, Woods Hole, Falmouth, USA
Learning latent low-dimensional dynamics from neural population responses: a stochastic control approach
Invited talk at Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
Learning latent low-dimensional dynamics from neural population responses: a stochastic control approach
Invited talk at Center for Computational Neuroscience, Flatiron institute, New York, USA
Geometric methods for inference of nonlinear systems
Invited talk at Gjiorgjieva lab, School of Life Sciences, Technical University of Munich, Munich, Germany
Deterministic particle solutions to Fokker-Planck equations
Invited talk at Institute of Mathematics, University of Potsdam, Potsdam, Germany
Revealing network physical interactions from event timing patterns: A model-free approach
Invited talk at Max Planck Institute for the Physics of Complex Systems, Dresden, Dresden, Germany