Representational Similarity Analysis

Understanding Representations in Minds and Machines
Erscheinungsjahr 2026
ISBN: 978-0-443-23563-4
Verlag: Elsevier Science

Buch, Englisch, 420 Seiten, Format (B × H): 152 mm x 229 mm

Representational Similarity Analysis: Understanding Representations in Minds and Machines is the first book on representational similarity analysis that surveys advances in computational neuroscience. First off, the book introduces the reader to representation patterns and relation to neuroscience and psychology. The second section explores how to understand the data, including data modalities in both modern neuroscience and AI research. The third section reviews Representational similarity analysis (RSA) in depth, covering all aspects from metrics, interpretation, and modeling. Next, the book offers tutorials of RSA computations, including setup, case studies, and practical considerations. The last section summarizes the possible future frontiers of representational studies.

Autoren/Hrsg.

Lin, Baihan

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Forschungsmethodik, Wissenschaftliche Ausstattung
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft

Inhaltsverzeichnis

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

Über Autor(innen)

Lin, Baihan
Baihan Lin is a neuroscientist at Columbia University. Before his PhD at Columbia, Baihan held a masters
degree in Applied Mathematics from University of Washington, and have worked at IBM, Google X, Microsoft, Amazon and BGI Genomics, where he pioneered various machine learning solutions in clinical healthcare domains such as computational psychiatry and personalized medicine. His research in reinforcement learning, deep learning, and natural language processing has been translated into deployed applications such as AI companion for therapists (INTERSPEECH-22), behavioral simulator for psychiatric disorders (IJCAI-19, AAMAS-20, HBAI-20), surrounding-aware virtual reality (IJCAI-20), adaptive prescriptor for epidemic control (CEC-22, FUZZ-22) and the first register-free diarization system (INTERSPEECH-20, ACML-21). He is also a main contributor to RSAToolbox, an open-sourced software that performs statistical inference on neural systems and neural nets. He has authored over 30 publications, filed over 20 US patents and reviewed for over 40 journals or conferences. He served as the Chair for the Society for Neuroscience (SfN) 2022 Symposium on Industrial Insights and Perspectives Into Translational Neuroscience and have organized various conference tutorials.