Dimitra Maoutsa

Dimitra Maoutsa

she/her

Postdoctoral researcher on Theoretical and Computational neuroscience

Publications

You can also find my articles on my Google Scholar profile.

First and last author publications

Learning to learn dynamical associations with reward-gated local plasticity

Published in ICLR-New Frontiers in Associative Memories workshop, 2026

Learning to learn input output associations.

Recommended citation: 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

Published in ICLR-Geometry-grounded Representation Learning and Generative Modeling workshop, 2026

Geometric motivation of the sparse SDE inference method.

Recommended citation: 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.

Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback

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.
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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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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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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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Other publications