Céline Drieu (she/her/hers)
Assistant Professor
Contact Information
- [email protected]
- Ames 221
- Group/Lab Website
Research Interests: Neuronal dynamics and circuits, learning and memory, cognitive maps, sleep and memory consolidation
Education: PhD
Celine Drieu is a systems neuroscientist and Assistant Professor in the Department of Neuroscience and the Department of Psychological and Brain Sciences. Her lab studies how distributed brain networks support learning and memory. She received her undergraduate training in Paris, France, earning a bachelor’s degree from René Descartes University and a master’s degree from Pierre and Marie Curie University. She completed her PhD in Neuroscience in the laboratory of Dr. Michael Zugaro at the Collège de France and conducted postdoctoral research at Johns Hopkins University with Dr. Patricia Janak.
The Drieu Lab investigates how distributed brain networks support learning and memory to enable adaptive behavior. We study both instrumental learning – how animals use sensory cues to guide actions and predict outcomes – and episodic memory, which enables the recall of specific experiences in their spatio-temporal context. In addition, we explore how these two processes interact across brain-wide circuits to support flexible decision-making and adaptation to changing environments.
To address these questions, we combine innovative behavioral paradigms in rodents with high-density, multi-site electrophysiology, two-photon calcium imaging, and closed-loop optogenetic manipulations. These approaches allow us to examine how neuronal ensembles coordinate across brain regions, how neural representations reorganize with experience and sleep, and how specific dynamics and circuits causally contribute to behavior. To interpret the rich datasets generated by these experiments, we develop and apply advanced computational methods to reveal the structure and dynamics of brain-wide activity underlying flexible learning. In collaboration with computational neuroscientists and theorists, we aim to build models of distributed brain computation to generate testable predictions that guide experiments and link neural dynamics to behavior.
This research hopes to provide fundamental insights into cognition and has important implications for understanding and treating memory- and reward-related disorders.