Christian Vestergaard is a CNRS researcher in the Decision and Bayesian Computation lab at the Pasteur Institute. He holds a PhD in theoretical physics and biophysics from the Technical University of Denmark (2012).
Topics of interest
Network modeling, neuroscience, graph learning, statistical physics.
Project in Prairie
Christian Vestergaard’s research focuses on linking the complex topology of the neural network that make up an animal’s brain to how it computes and generates behavior. He develops statistical and computational methods drawing inspiration from graph theory, statistical physics, information theory, and machine learning. This project aims at providing new approaches to artificial neural network design and optimization.
Universal coding theorems show that a multitude of different neural architectures can be used to represent any function. Thus, the intricate architecture of biological neural networks probably determine not what they can learn, but rather how they encode information in order to provide good inductive biases that enable robust and efficient learning. Focusing on small animals, such as Drosophila larva, whose neural wiring has been mapped at full resolution and whose neurons can be individually controlled in freely behaving animals, will allow us to link the structures of link neural microcircuits to their functions. This will help uncover how biological neural networks differ from artificial neural networks and may provide inspiration for more efficient deep learning architectures.