My Research

I find staggering the Human brain’s ability to make choices that are more flexible, efficient and robust than today’s most powered machines, in a bewilderingly complex world and under scarce resource. The overarching goal of my research is to understand the computational principles and limitations that underlie Human adaptive and efficient decision-making.

Focus: I study how neural networks integrate context with sensory information to inform decision-making, within and across brain regions, by combining high density recording & fMRI techniques, psychophysics, data-constrained computational modeling. My research focuses on the following questions:

Approach: I combine system neuroscience neural recording techniques, such as extracellular electrophysiology in rodents and fMRI in humans, with psychophysical experiments, to jointly measure neural population activity and behavior and I use decision theoretic and neural network models to link them. To thoroughly describe the dynamic computations reflected in the recorded activity, I analyse how neural network connectivity pattern shapes and constrains the representational and computational capacity of recurrent neural network models of the cortical microcircuit at different levels of biophysical detail. To do so I use mathematical tools from linear dynamical system theory and machine learning techniques of dimensionality reduction. High-density neuropixels multi-unit recordings combined with machine learning-driven progress in spike sorting, have reduced sampling biases, such as the preferential selection of task-responsive neurons. Yet the amount and types of sorting biases that limit our ability to measure the true population activity are unclear. I use biophysically-detailed models of the neocortical microcircuit to characterize and quantify spike sorting biases and improve spike sorting algorithms.

Some context: I currently lead research as a postdoc in Michael Reimann's lab at the Blue Brain Project, EPFL. I create experimental, computational approaches and machine learning tools to analyse stimulus and context encoding in large-scale biophysically-detailed simulations of the entire primary somatosensory cortex of rodents, relying on the computing powers of high-performance clusters and graphical processing units (GPU). Before joining the Blue Brain Project, I worked with Prof. Justin Gardner at Stanford University, where I used psychophysics, fMRI, and Bayesian modeling to link human visual choices with cortical activity. I studied Computational Neuroscience at the University of Bordeaux, France, where I obtained my PhD under the supervision of Prof. Thomas Boraud. During my PhD, I performed multi-electrode array (MEA) recordings in the Basal Ganglia of primates trained to perform a stochastically rewarded two-choice task. I specialized in developing statistical approaches and reinforcement learning models to link the animal choices with the measured MEA spiking activity. I found that choices are not always motivated by reward but are sometimes entirely driven by task contextual cues. I pursued this research first at the Riken Brain Science Institute in Japan, then at Stanford University. During this time, I honed my skills in machine learning and statistical decision modeling and acquired new skills in fMRI to link human decision-making, prior experience, and cortical activity. I then spent a brief period in industry working as a Lead Data Scientist where I perfected my machine learning engineering skills. I currently also pursue research as a visiting scholar at KU Leuven, where I investigate how artificial networks can self-organize to represent information efficiently and make adaptive decisions in collaboration with Prof. Cees Van Leeuwen.

My CV

Academic appointments

Industry appointments

Awards & Fellowships

Peer-reviewed publications

Teaching

Seminars

Peer-reviewed International Conference Proceedings

Peer-reviewed national Conference Proceedings

Non peer-reviewed

Open source software

Self-organizing networks

Visual inference

fMRI voxel modeling

Deep learning for image processing

Deep learning for language modeling

My contributions to large collaborations:

Mentoring & acknowledgements