Computational neuroscience and Artificial Intelligence
Track directors: Jonathan Vacher (Université Paris Cité) et Benoît Girard (CNRS / Sorbonne Université)
Capacity: 15
General organization
The master’s student must register to one of the tracks. These tracks ensure the acquisition of genuine expertise in the concepts, methods, and techniques specific to each discipline, enhancing the clarity of skills associated with the diploma. The program establishes a common cultural foundation from the first year (M1) through a core curriculum and introductory courses to the different disciplines. In the second year (M2), the majority of courses are fully interdisciplinary and open to students from all tracks. Our goal is to cultivate cognitivists equipped with both robust disciplinary expertise and a broad interdisciplinary culture, essential elements for fostering meaningful collaboration across disciplines.
Specific to this track
Training
The Computational Neuroscience and Artificial Intelligence (CNIA) program provides comprehensive interdisciplinary training at the intersection of cognitive sciences, computational neuroscience, and artificial intelligence. By combining rigorous theoretical approaches and advanced digital tools, this program aims to develop experts capable of modeling and analyzing the natural and artificial mechanisms of cognition.
Prerequisites
This program is designed for students with a dual skillset or strong interest in complementary disciplines. Ideal profiles include:
– Students in mathematics or computer science with an interest for neurosciences, psychology or biology.
– Or conversely, students in neurosciences, psychology or biology with training in mathematical and computational tools.
Career Opportunities
Graduates of the Computational Neuroscience and AI program will be equipped for diverse careers in:
– Academic and applied research (computational neuroscience, AI, cognition).
– Tech industries (AI, data processing, digital health).
– Expertise in technology and data ethics.
Keywords : Computational neuroscience, Artificial intelligence, Machine learning, Cognitive science, Bayesian modeling, Neural networks, Neuroimaging, Data analysis, Science and technology ethics
M1 – Semester 1
Master core curriculum
CORE-4 Ethics in cognitive sciences (3 ECTS), Katie Evans, Anouk Barberousse, Raja Chatila
Master options: 3 to 6 ECTS among these options
CORE-1 Experimental approach (3 ECTS), Christophe Pallier
CORE-2 Data camp (3 ECTS), Christophe Pallier, Mehdi Khamassi
PROG-101Introduction to programming (3 ECTS), Sylvain Charron
Track core curriculum
NEURO-101 Introduction to Cognitive Neurocience (3 ECTS), Chloé Berland, Pierre Bourdillon
NEURO-301 Basic methods in Neuroimaging (3 ECTS), Laura Dugué
PSYCH-101 Introduction to Cognitive Psychology (3 ECTS), Thérèse Collins
NCIA-101 Introduction to Computational Neuroscience and AI (3 ECTS), Mehdi Khamassi, Benoît Girard
NCIA-201 Bayesian Modeling of brain and behavior (3 ECTS), Jonathan Vacher, Jean Daunizeau
NCIA-202Adaptive collective systems (3 ECTS), Nicolas Bredèche, Aurélie Beynier, Nicolas Maudet
Options : 3 to 6 ECTS among these options
INT-101 Internship (1 day/week) (6 ECTS)
NEURO-302 Advanced methods in Neuroimaging (3 ECTS), Laura Dugué
PHILO-101 Scientific reasoning (6 ECTS), Anouk Barberousse
LING-101 Introduction au Traitement Automatique des Langues (in French) (3 ECTS), Guillaume Wisniewski
LING-301 Machine Learning for Natural Language Processing : the fundamentals (6 ECTS), Marie Candito
NEURO-201 Neuropsychology as a central paradigm in cognitive science (3 ECTS), Lucie Rose, Paolo Bartolomeo, Laurent Cohen
NEURO-SU-1 Fundamentals in Neurobiology (3 ECTS), Régis Lambert, Stéphanie Daumas
INFO-SU-1 Logique et représentation des connaissances (in French) (6 ECTS), Nicolas Maudet, Gauvain Bourgne
EXT-101 (external course) (6 ECTS)
EXT-102 (external course) (3 ECTS)
M1 – Semester 2
Master core curriculum
CORE-3 Literature (Meta-)review (3 ECTS), Jonathan Vacher
Master options: 3 to 6 ECTS among these options
CORE-1 Experimental approach (3 ECTS), Sylvain Charron
CORE-2 Data camp (3 ECTS), Christophe Pallier, Mehdi Khamassi
PROG-202Human experimental workshop (3 ECTS), Mark Wexler
Track core curriculum
NCIA-203 Computational Neuroscience (3 ECTS), Bruno Delord
NCIA-204 Reinforcement learning applied to Cognitive Science (3 ECTS), Mehdi Khamassi, Benoît Girard
INFO-SU-2 Machine Learning (1st half) (lectures in French, slides and practical labs in English) (3 ECTS), Nicolas Baskiotis
Options : 12 to 15 ECTS among these options
INT-201 Internship (1 day/week) (mandatory if not taken during S1) (6 ECTS)
NCIA-205 Hacking Cognition (3 ECTS), Laura Dugué
NCIA-206 Model-based neuroimaging (3 ECTS), Laura Dugué
NEURO-202 Advanced Cognitive Neuroscience (6 ECTS), Louise Kirsch
LING-208 Theory and pratice of large language models (6 ECTS), Guillaume Wisniewski open in 2026
SOC-301 Applying methods cognitive sciences (3 ECTS), Thérèse Collins open in 2026
INFO-SU-3 Machine Learning (2nd half) (lectures in French, slides and practical labs in English) (3 ECTS), Nicolas Baskiotis
INFO-SU-4 Interfaces Humain-Machine (lectures in French, slides in English) (6 ECTS), Gilles Bailly, Baptiste Caramiaux
INFO-SU-5 Intelligence Artificielle et Manipulation Symbolique de l’Information (in French) (6 ECTS), Christophe Marsala, Gauvain Bourgne
INFO-SU-6 Apprentissage pour la Robotique (6 ECTS), Olivier Sigaud, Nicolas Bredèche, Stéphane Doncieux
MBIO-SU-1 Modèles mathématiques en neurosciences (in French) (6 ECTS), Michèle Thieullen, Delphine Salort
M2 – Semester 3
Master core curriculum
INT-301 Internship preparation (1 day/week) (6 ECTS)
Options : 18 ECTS among these options
COG-405 Computational psychiatry (6 ECTS), Fabien Vinckier, Philippe Domenech, Renaud Jardri
NCIA-207 Advanced bayesian modeling and model comparison (6 ECTS), Jonathan Vacher, Jean Daunizeau
NCIA-208 Integrative modeling of living organisms (3 ECTS), Romain Brette
NCIA-209 Advanced Reinforcement learning applied to Cognitive Science (3 ECTS), Mehdi Khamassi, Benoît Girard, Olivier Sigaud
NCIA-210 Human-AI Interactions (6 ECTS), Gilles Bailly, Baptiste Caramiaux
NCIA-211 Advanced Computational Neuroscience (3 ECTS), Bruno Delord
CORE-4 Ethics in Cognitive Sciences (only for direct entrance to M2) (3 ECTS), Katie Evans, Anouk Barberousse, Raja Chatila
IPS-SU-1 Human experimentation and statistics (only for direct entrance to M2) (3 ECTS), Stéphane Genet, Emmanuel Guigon
MVA-1 Algorithms for speech and natural language processing (6 ECTS), Emmanuel Dupoux
Closed-loop neuroscience (3 ECTS), Valérie Ego-Stengel
CNN-Saclay-2 Dynamical system and computational neuroscience (3 ECTS), Antoine Chaillet
Options : 6 ECTS among these options
Attention (6 ECTS), Laura Dugué
COG-402 Decision (6 ECTS), Mathias Pessiglione
LING-218 Computational language modeling and cognition (6 ECTS), Benoît Crabbé
MMA-UPC-1 Machine learning and optimization (6 ECTS), Jonathan Vacher
MMA-UPC-2 Generative modeling of images (6 ECTS), Jonathan Vacher
INFO-SU-7 Advanced machine learning (6 ECTS), Patrick Gallinari
INFO-SU-8 Reinforcement learning (RL) (6 ECTS), Olivier Sigaud, Benjamin Piwowarski
INFO-SU-9 Reconnaissance des formes et IA (in French) (6 ECTS), Matthieu Cord
INFO-SU-10 Explainable AI (6 ECTS), Marie-Jeanne Lesot
INFO-SU-11 IA pour la robotique et les sciences cognitives (lectures in French, slides in English) (6 ECTS), Nicolas Bredèche
MBIO-SU-1 Modèles probabilistes en neurosciences (in French) (6 ECTS), Michèle Thieullen
MBIO-SU-2 Fonctionnement des réseaux de neurones : analyse mathématique (in French) (6 ECTS), Delphine Salort
iMOV-SU-1 Vision from retina to primary visual cortex (6 ECTS), Gaël Orieux, Grégory Gauvain
iMOV-SU-2 Physiology of perception (6 ECTS), Olivier Marre, Grégory Gauvain
MSR-SU-1 Motor control (3 ECTS), Emmanuel Guigon, Malika Auvray
MSR-SU-2 Social robotics (6 ECTS), Mohamed Chetouani
M2A-SU-2 Introduction à l’apprentissage automatique (in French) (3 ECTS), Maxime Sangnier
M2A-SU-3 Méthodes du premier ordre pour l’optimisation non convexe et non lisse (in French) (3 ECTS), Pauline Tan
M2A-SU-4 Optimisation stochastique & généralisation pour le ML (in French) (3 ECTS), Claire Boyer
M2 – Semester 4
Master core curriculum
INT-401 Internship (full time) (30 ECTS)