M2 CNS - Teaching Program
Computational Neurosciences
This Master program in Computational Neurosciences combines a solid theoretical foundation with hands-on practical work. In the first part of the program, students will follow courses on the fundamentals of neuroscience, mathematics, programming, and statistics to prepare for advanced courses in the second part of the program. Students will develop practical skills through project-based learning. Optional units are offered to specialize in cutting-edge areas of computational neuroscience, from clinical approaches, cognition, artificial intelligence or even neurotechnologies. The second semester is fully dedicated to laboratory internships.
First Semester
Disciplinary Courses
Scientific Tools
Computational models of neurophysiological phenomena
The aim of this teaching unit is to train students in recent methodological advances in the modeling of neuophysiological activities, such as the electrical activity of the brain recorded by electrophysiology. These techniques are extremely powerful in linking spatial scales and different recording techniques in neuroscience, from the single cell to neural circuits up to interacting brain areas. This training will be strongly based on practical work, to develop and implement (in Python) in silico models of neurons, models of populations of neurons (neural networks) and models of networks of populations of neurons (brain networks) on a whole-brain scale.
Coordinator/s: Matteo di Volo (matteo.di-volo@univ-lyon1.fr)
Computational Models of Behavior and Cognition
The aim of this teaching unit is to train students in computational models of the major cognitive functions (learning, decision-making, executive functions such as attentional control and the role of working memory…), in particular through their influence on behavior (our choices, our errors and our speed of response). We will also cover how these models can be used to explore the neural basis of these cognitive functions. This teaching unit will focus in particular on the major families of models such as artificial neural networks, reinforcement learning and probabilistic or Bayesian approaches to cognition.
Coordinator/s: Jérémie Mattout (jeremie.mattout@inserm.fr) & Matteo di Volo (matteo.di-volo@univ-lyon1.fr)
Computational Neurosciences
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, engineers, as well as clinicians and aims at familiarizing students with scientific modelling in the field of Neuroscience. Conference series are given by national and international experts.
More info: NeuroComp
Coordinator/s: Matteo di Volo (matteo.di-volo@univ-lyon1.fr) & Jérémie Mattout (jeremie.mattout@inserm.fr)
Neuroconferences
Scientific conferences given by French and international experts on hot topics in the different levels of analysis covered by Neurosciences. Topics are renewed each year. Extensive time is dedicated to discussions between the students and the speakers, aiming to develop in-depth scientific reasoning and networking.
Coordinator/s: Marion RICHARD (marion.richard@univ-lyon1.fr)
Statistics
This teaching unit aims at mastering methods used for understanding, selecting and using appropriate and advanced statistics in biology such as non parametric tests, ANOVA (>2 factors), mutiple regressions (linear, logistic, mixed), PCA/CFA, and classification.
More info: Stats
Coordinator/s: Philippe BOULINGUEZ (philippe.boulinguez@univ-lyon1.fr) & Marion RICHARD (marion.richard@univ-lyon1.fr)
English
English courses specifically designed for scientific communication. This includes writing of scientific articles, training for poster presentation, job interviews and writing letters for editors/reviewers.
More info: English
Coordinator/s: Nathalie DOURLOT (nathalie.dourlot@univ-lyon1.fr)
Elements of Neurosciences, Mathematics and Informatics
Coordinator/s: Matteo di Volo (matteo.di-volo@univ-lyon1.fr)
Optional Units
Artificial Intelligence and Cognition
This course sets out the fundamentals of the field of Artificial Intelligence (AI), enabling us to understand the different approaches (symbolic AI, digital AI, computational AI, strong AI, etc.). This course will define the relationship between AI and cognition, as well as the cognitivist and constructivist positions, enabling an understanding of the different currents in AI, their contributions and limitations. A particular focus will be placed on developmental AI, which is currently booming.
More info: AI Cog
Coordinator/s: Marie LEFEVRE (marie.lefevreuniv-lyon1.fr)
Advanced Clinical Neurosiences
Conference series by national and international experts presenting different levels of analysis covered by clinical neurosciences. A special emphasis is given to cutting-edge methodologies used to study the neural underpinning of brain dysfunctions in neurological and psychiatric diseases. Extensive time is dedicated to discussions between the students and the speakers, aiming to develop in-depth scientific reasoning and networking.
Coordinator/s: Irene CRISTOFORI (irene.cristofori@univ-lyon1.fr) & Frédéric HAESEBAERT (frederic.haesebaert@ch-le-vinatier.fr)
Human-Computer Interaction: Robotic Interfaces
The design or correction of digital interfaces currently represents a major part of Brain machine interfaces activity. This course will present the different players involved in designing a digital interface and how they work together. The course will focus on presentations of the different methods available to the designer. Finally, students will be asked to design an interface of their choice in small groups.
Coordinator/s: Emanuelle REYNAUD (emanuelle.reynaud@univ-lyon2.fr)
Second Semester
Internship
Full-immersion in the world of research in Computational Neurosciences for a period of 5 to 6 months. Students can do their internship in Lyon, in other French laboratories or abroad.