Computational neuroscience aims to understand the brain through theoretical, mathematical and computational means. This field is in full expansion leading to new tools to understand and study the brain at different scales, to simulate neuronal and cognitive mechanisms, and to build programs or machines having properties mimicking those of the nervous system.
This course is meant to be accessible to biologists, psychologists, as well as engineers. The aim of this unit is to lay the theorical and methodological foundations for investigation in the field of computational neuroscience. The courses will first discuss the main theorical, historical and philosophical principles of computational modeling, and introduce some applications (robotics, artificial intelligence, expert systems, etc…).
Next, relevant mathematical approaches will be presented, including simple differential equations, Bayesian statistics, neuronal simulations, as well as some introduction to machine learning. Practical courses will target, on the one hand, the modeling of behavioral and cognitive processes and, on the other hand, the modeling of neurons and neuronal networks.
Importantly in this course, we touch upon multiple aspects, including theoretical principles and the rationale of computational modelling, the link with other fields such as robotics and artificial intelligence and the classical mathematical tools used in this area of Neurosciences, building on concrete examples (ex. Behavioral data in decision making task).
