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.
CV
Publications
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.
From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints
(2025). "From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints." under review
Identifying plasticity mechanisms underlying experience-driven adaptation in cortical circuits
Maoutsa, D; Gjorgjieva, J. (in preparation). "Identifying plasticity mechanisms underlying experience-driven adaptation in cortical circuits." (in preparation). .
Plasticity-driven circuit self-organization on spiking stabilized supralinear networks
Adell Segarra,R; Festa, D; Maoutsa, D; Gjorgjieva, J. (in preparation). "Plasticity-driven circuit self-organization on spiking stabilized supralinear networks." (in preparation).
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
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