Heike Stein

Group leader

I'm a computational neuroscientist interested in data-driven modeling of behavioral dynamics and its neural control. My long-term goal is to develop models that are useful for understanding neural computation in increasingly naturalistic contexts. Before starting as a CNRS researcher at the Institute for Intelligent Systems and Robotics (ISIR) at Sorbonne Université, I was a postdoc in Alex Cayco-Gajic's lab at ENS, where I developed tools for large-scale neural data analysis and modeled locomotion in complex environments. During my PhD at Universitat de Barcelona in Albert Compte's lab, I studied the role of NMDA receptor hypofunction in working memory performance and trial history biases. Download CV

Sepehr Saeedpour

Master student (M2)

I am a Computational Neuroscience M.Sc. student at ENS-PSL. I am broadly interested in questions where abstract mathematics intersects with something recognizably alive. These days, I develop Gaussian Process state space models (GP-SSMs) to characterize the dynamics of head-direction circuits. These circuits integrate vestibular signals and visual landmark information to maintain a stable internal estimate of heading and the central question of my work is how these input streams interact to govern the accuracy, robustness, and reorientation of the population-level representation.

Jessica Lau

Master student (M1)

I am a Master’s student in Cognitive Science at ENS-PSL, in the cognitive modelling, neurotheory, and AI track. My research interests broadly focus on computational modelling of cognitive processes and behaviors, including decision-making, motor behaviors, and memory. Some of my previous research experiences have included investigating the role of dopamine in reinforcement learning models of reward-seeking behavior in mice, and exploring the role of social cognition in functional outcomes in clinical populations. In my current internship in the lab, I am adapting an unsupervised machine learning algorithm called Keypoint MoSeq for social behavioral modelling in mice. More precisely, I am extending the current algorithm, which relies on an autoregressive hidden markov model (AR-HMM), to capture interaction dynamics.

Bennet Sakelaris

Postdoc

I am a postdoctoral researcher working on data-driven modeling of neural activity. I am broadly interested in understanding how neural circuits generate behavior, learn from experience, and maintain stable representations in changing environments. Previously, I completed my PhD in Applied Mathematics at Northwestern University under the supervision of Hermann Riecke, where I studied a range of biological systems, including learning the olfactory system, transcriptional dynamics in yeast, and whole-brain activity in C. elegans.