I. Introduction to representation patterns 1. What is a representational pattern? 2. Representations in neuroscience: the computational mechanisms of the brain 3. Representations in psychology: the symbolic structures of cognition 4. Representations in deep learning: the black box of deep neural networks II. Understanding the data 5. Data modalities in modern neuroscience and AI research 6.

Methods studying the brain functions 7. Related fields: information theory, network science, multivariate, Bayesian, optimization 8. Effective visualizations of neural data 9. Experimental design for representational studies III. Representational similarity analysis (RSA) 10. A practical example: do monkeys and humans share visual representations? 11. The representational similarity framework 12. Everything about dissimilarity measures 13.

Everything about model comparison and statistical inference 14. Everything about interpretation and visualization IV. Tutorials of RSA computations 15. Tutorial setup 16. Hands on examples with case studies 17. Practical considerations V. Frontiers of representational studies 18. Sensory perception 19.

Learning and memory 20. Language and speech processing 21. Motor learning 22. Emotions and affect 23.Attention mechanisms 24. Interacting and social brains 25. Psychiatry and clinical studies 26. Interpretable and neuroscience-inspired AI.