This course gives an introduction to the modeling in
neuroscience - a domain called computational neuroscience -, making use of tools from information theory, Bayesian inference, dynamical systems, and statistical physics.
The main topics to be discussed are memory, neural coding and decision making in natural neural systems.
The course highligts the links and interactions between computational
neuroscience, signal processing and machine learning. It also
provides openings to other themes - in particular complex systems in the
social sciences.
In terms of form, the course is as much about formal (mathematical),
algorithmic and quantitative aspects, as it is about qualitative ones
(historical aspects, conceptual contributions of modeling,
interpretation of model analysis for the understanding of human and
animal cognition).
- Enseignant: Jean-Pierre NADAL