Representational Similarity Analysis
Understanding Representations in Minds and Machines
- 1st Edition - September 1, 2026
- Latest edition
- Author: Baihan Lin
- Language: English
Understanding the representations of artificial or biological neural networks is crucial in discovering the neural information processing mechanisms of the brain. Re… Read more
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.
- Provides a timely and comprehensive review of representational similarity analysis
- Proposes a new unified formulation of statistical inference methods for comparing brain and model representations
- Covers a vast array of special topics and applications including both vision and language to help illustrate the wide use in understanding neural information processing
- Presents convincing case studies and hands-on tutorials for a broad audience of scientists including neuroscience, psychology, and computer science
Researcher and students in general neuroscience, computational neuroscience, and cognitive neuroscience
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
6.1 Classical univariate analysis
6.2 Multivariate decoding methods
6.3 Voxel encoding methods
6.4 Pattern component modeling
6.5 Network neuroscience
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
11.1 What are the assumptions underlying RSA?
12. Everything about dissimilarity measures
12.1 What is a distance metric, and which one should we pick?
12.2 Why are RDMs positively biased, and what can we do about it?
12.3 What is multivariate noise normalization?
12.4 What is a noise ceiling?
13. Everything about model comparison and statistical inference
13.1 What is the right way of comparing representational dissimilarity matrices (RDMs)?
13.2 How to perform statistical inference to evaluate big neural-activity models and data?
13.3 How to improve the reliability of our RDMs?
13.4 What is searchlight representational similarity analysis?
14. Everything about interpretation and visualization
14.1 What are the limitations in the interpretability of RDM similarities?
14.2 How to visualize the representational dynamics?
14.3 How to generalize the summary statistics from representational geometry to topology?
IV. Tutorials of RSA computations
15. Tutorial setup
15.1 Example dataset: interpreting the deep neural networks
15.2 Example dataset: studying visual perception with Allen Brain Observatory
16. Hands on examples with case studies
16.1 Python RSAToolbox tutorial
16.2 Python NeuroRA tutorial
16.3 Python Score-CAM tutorial
16.4 Matlab rsatoolbox tutorial
16.5 R RSA tutorial
17. Practical considerations
17.1 Working with neural network models in PyTorch and Tensorflow
17.2 Data processing and management for large datasets
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
- Edition: 1
- Latest edition
- Published: September 1, 2026
- Language: English
BL