@esther-poniatowski
PhD candidate in Cognitive Neuroscience at the École Normale Supérieure (ENS), Paris. My research focuses on understanding how neuronal networks encode and process information, particularly in context-dependent tasks. It employs both theoretical modeling with empirical data to investigate the computational mechanisms underlying flexible behavior.
- Research Interests: Neuronal Networks, Deep Networks, Recurrent Networks, Representational Geometry, Information Processing, Distributed Coding, Computational Cognitive Science, Theoretical Modeling
- Programming Languages: Python
- Machine Learning Frameworks: PyTorch, scikit-learn
Utilities:
- meandra: Framework for modular and reproducible data workflows with structured configuration management.
- tessara: Parameter management toolkit supporting constraints, rules, and modular compositions.
- khimera: Plugin automation framework for seamless integration on both host and provider sides.
- janux: Utility for automating secure server connections.
Research:
- multitask-context-dependent-behavior: Analysis of neuronal recordings from animals performing multiple tasks under varying attentional states.
- Massi2022: Comparison of reinforcement learning strategies inspired by hippocampal replay for robotic navigation.
Teaching:
- ThNeuro-UlmM2: Teaching materials and solutions for theoretical neuroscience courses.
- https://github.com/esther-poniatowski/exercise-sheet-tex-toolkit
- exercise-sheet-tex-toolkit: LaTeX macros and styles for designing educational resources.
- Massi, E., Dromnelle, R., Mailly, J., Barthéléemy, J., Canitrot, J., Poniatowski, E., Girard, B., & Khamassi, M. (2022). Model-based and model-free replay mechanisms for reinforcement learning in neurorobotics.
- Email: [email protected]
: Esther Poniatowski
For discussions on research, collaborations, or access to specific repositories, please reach out via email or LinkedIn.
Last updated: 2025-05-11