2018-03-26 : PhD Program: course in Computational Neuroscience

PhD Program: course in Computational Neuroscience.

PhD Program: course in Computational Neuroscience


Computational neuroscience is an expending field that is proving to be essential in neurosciences. The aim of this course will be to provide a common solid background in computational neurosciences. The course will comprise historical recall of the field and a description of the different modelling approaches that are currently developed, including details about their specificities, limits and advantages.


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.

Language of intervention


Number of hours

~20 hours (session 1=7 + session 2=7 + session 3=4)

Max participants

15 for the practical sessions (afternoon Day 2 and Day 3), unlimited for theoretical courses

Public priority

PhD students

Public concerned

PhD students, interested M2 students and postdocs


Institut des Neurosciences de la Timone (INT)


neuronal modelling, neural circuit modelling, information theory, decoding and encoding


Understanding how computational modelling can be used to formulate and solve neuroscience problems at different spatial and temporal scales; learning the formal notions of information, encoding and decoding and experimenting their use on toy datasets


First session: Introduction to modeling single neurons (morning); An introduction to neural masses: modeling assemblies of neurons up to capturing collective oscillations and resting state dynamics in a mean-field model - presentation of the Virtual Brain software (afternoon) - Second session: An overview on “What is encoding?” “What is decoding?”: formalization of the notion of information in neural activity; shared and transferred information; integration, segregation and complexity (morning). Bayesian probabilities, the Free-energy principle and Active Inference, with practical demonstrations in python (afternoon). Third session: the problem of information estimation in practice. Practical exercices in Matlab: estimating entropy and stimulus decodability from spike trains; comparing coding hypotheses (morning).


Basic knowledge of statistics and probability and calculus (differential equations,…) is useful, but steps will be explained and complex math avoided as much as possible. Practical exercises are in python and/or MATLAB, so basic knowledge of these environments is a plus.


day 1 : 2018-03-26 : an introduction to Computational Neuroscience

day 2 : 2018-03-27 : Information theory / bayesian models

day 3 : 2018-03-28 : Practical course on Information theory

  • 09:30-12:30 = Practical course on Information theory (DaB)

day 1 - morning : the single neuron

day 1 - afternoon : neural mass models

day 2 - morning : information theory

day 2 - afternoon : bayesian models


Sponsored by

Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.