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Teaching in computational neuroscience:

• Machine Learning, Statistics, Dynamical Systems, Network Theory, Neuromodeling: gitlab repo
• Hackathon with project-based learning and tutorials BrainHack Marseille
• Dissemination (in French) at Université du Temps Libre of AMU: L’intelligence artificielle et la médecine de demain

Older teaching material:

• Python notebooks for EC estimation from fMRI data: https://github.com/mb-BCA/notebooks_review2019
• Python scripts for EC estimation and interpretation from multivariate Ornstein-Uhlenbeck (MOU) model: https://github.com/mb-BCA/pyMOU
• Python scripts for analysis of network flow for MOU process: https://github.com/mb-BCA/NetDynFlow
• Toy models about connectivity estimation, network analysis, machine learning, etc.: python code
• Presentation slides on whole-brain fMRI analysis from talk at conference Coupling and Causality in Complex Systems in Köln (Germany)
• Presentation slides for lecture on synaptic plasticity in master at Maastricht University