Principal investigator of the Decision and Bayesian computation laboratory, visiting scientist at Janelia research campus (2014-2019), visiting scientist at Institut Curie (2013), recipient of the StartULM (2018), C’nano innovation (2017) and Young researcher price of the French Biophysics Society (2009).
Topics of interest
Bayesian inference, neuroscience, biological decision-making, statistical physics and data processing in virtual reality
Project in Prairie
Jean-Baptiste Masson will focus on Bayesian induction, structured inference, physical environment modeling and statistical physics to probe learning in insect brains using neuronal, connectome and behavior imaging. He will organize every two years symposium on links between neuroscience, AI and physics; and a graduate class on structured inference.
How are insects able to perform complex probabilistic tasks by leveraging only small small neural networks, whereas machine-learning tasks often require large-scale architectures and extensive training on massive datasets? Evolution is able to shape decision-making in small neural circuits while maintaining high performance. By joining physical modelling, supervised and unsupervised structured inferences, Bayesian induction and numerical simulations, we can probe how evolution programmed robust decision making in the “brain“ of insects. In turn, we can extract key neural circuits from these insects and test their performance in real-world tasks.