Published in NeurIPS-WiML workshop, 2025
Proposed a method for biologically plaussible training of RNNs in reinforcement learning settings.
Recommended citation: Maoutsa, D.(2025). "Discovering neo-Hebbian plasticity rules for reward-driven training of RNNs." NeurIPS-WiML workshop.
Published in NeurIPS 2025 - Symmetry and Geometry in Neural Representations workshop, 2025
Proposed a method for meta-learning three-factor plasticity rules for biologically plaussible training of RNNs in reinforcement learning settings.
Recommended citation: Maoutsa, D.(2025). "Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback." NeurIPS-NeurReps workshop.
Download Paper
Published in under review, 2025
Recommended citation: (2025). "From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints." under review
Download Paper
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). .
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.
Download Paper
Published in Technical University of Berlin, 2023
PhD dissertation.
Recommended citation: Maoutsa, D. (2023). "Deterministic particle flows for stochastic nonlinear systems: Simulation, Control, and Inference." Technical University of Berlin. 1(3).
Download Paper
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.
Download Paper
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.
Download Paper
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).
Download Paper
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.
Download Paper | Download Slides
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.
Download Paper
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).
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).
Download Paper