Statistical modeling in neuroscience
One of the most fascinating property of the brain is its ability to extract relevant information from the environment. How the brain learns to extract relevant features is at the heart of this research project. Indeed, even though this feature extraction is done in a seemingly effortless way, the required computation steps to make sense of the environment are far from being trivial. How to reconstruct the 3D shape of an object which is only perceived on a 2D retina? How to recognize an object if only part of it is observed or if it is observed from a new perspective? How to optimally combine multi-sensory cues given that each sensor has its own reliability? How to extract the melody of a single instrument when many of them are playing simultaneously? Interestingly, all those psychophysical tasks which deal with uncertainty can be formulated in a generic probabilistic (Bayesian) framework. Despite the increasing interest in this Bayesian approach, there is still one important question that remains unanswered: how is this learning and inference implemented in the brain at the level of single synapses and at the level of spiking neurons? This question is at the center of this research project. More generally this project is about developing new statistical models and apply them in the field of neuroscience.