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Full Description
Understanding the representations of artificial or biological neural networks is crucial in discovering the neural information processing mechanisms of the brain. Representational Similarity Analysis (RSA), is an analytical framework in computational and cognitive neuroscience, comparing models and brains in terms of their representational geometries. Representational Similarity Analysis: Unlocking the Neural Representations of Brains and Machines is the first book on representational similarity analysis, surveying the advances in computational neuroscience. This book is organized into five distinct sections. The first, 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, section offers tutorials of RSA computations including setup, case studies and practical considerations. The last section summaries the possible future frontiers of representational studies.
Contents
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
