The course aims at introducing students with the major tools that will be necessary during their thesis to model or analyze their neuroscientific results. While it will start by a short, generic introduction, we will then explore different systems at different scales. On the first day, we will study the different possible regimes in which a single neuron can behave, while progressively introducing the theory of dynamical systems to understand these more globally. Then, during the second day, we will introduce methods to analyze neuroscientific data in general, such as Bayesian methods and information theory. This will be implemented by simple practical examples.
Day 1 : An introduction to Computational Neuroscience (March 6, 2017)
09:15-09:30 = A (short) introduction to the field of Computational Neuroscience (Laurent Perrinet)
09:30-12:30 = Introduction to modeling single neurons (Laurent Pezard)
14:00-17:00 = An introduction to neural masses : modeling assemblies of neurons up to capturing resting state dynamics in a mean-field model – presentation of the Virtual Brain software (Demian Battaglia)
Day 2 : Information theory / bayesian models (March 13, 2017)
09:15-10:30 = An overview on “What is encoding?” “What is decoding?”: formalization of the notion of information in neural activity (Demian Battaglia)
11:00-12:15 = (…continued after the coffee break: ) Live information! From sharing information to transferring information (and a glimpse into the zoo of higher-order friends) (Demian Battaglia)
14:00-17:10 = Probabilities, Bayes and the Free-energy principle (Laurent Perrinet).