Visual Population Codes

Toward a Common Multivariate Framework for Cell Recording and Functional Imaging
Overview

Vision is a massively parallel computational process, in which the retinal image is transformed over a sequence of stages so as to emphasize behaviorally relevant information (such as object category and identity) and deemphasize other information (such as viewpoint and lighting). The processes behind vision operate by concurrent computation and message passing among neurons within a visual area and between different areas. The theoretical concept of "population code" encapsulates the idea that visual content is represented at each stage by the pattern of activity across the local population of neurons. Understanding visual population codes ultimately requires multichannel measurement and multivariate analysis of activity patterns. Over the past decade, the multivariate approach has gained significant momentum in vision research. Functional imaging and cell recording measure brain activity in fundamentally different ways, but they now use similar theoretical concepts and mathematical tools in their modeling and analyses.

With a focus on the ventral processing stream thought to underlie object recognition, this book presents recent advances in our understanding of visual population codes, novel multivariate pattern-information analysis techniques, and the beginnings of a unified perspective for cell recording and functional imaging. It serves as an introduction, overview, and reference for scientists and students across disciplines who are interested in human and primate vision and, more generally, in understanding how the brain represents and processes information.

Table of Contents

  1. Series Foreword
  2. Preface
  3. Introduction: A Guided Tour through the Book
  4. I. Theory and Experiment
  5. Grandmother Cells and Distributed Representations

    Simon J. Thorpe

  6. 2. Strategies for Finding Neural Codes

    Sheila Nirenberg

  7. 3. Multineuron Representations of Visual Attention

    Jasper Poort, Arezoo Pooresmaeili, and Pieter R. Roelfsema

  8. 4. Decoding Early Visual Representations from fMRI Ensemble Responses

    Yukiyasu Kamitani

  9. 5. Understanding Visual Representation by Developing Receptive-Field Models

    Kendrick N. Kay

  10. 6. System Identification, Encoding Models, and Decoding Models: A Powerful New Approach to fMRI Research

    Jack L. Gallant, Shinji Nishimoto, Thomas Naselaris, and Michael C. K. Wu

  11. 7. Population Coding of Object Contour Shape in V4 and Posterior Inferotemporal Cortex

    Anitha Pasupathy and Scott L. Brincat

  12. 8. Measuring Representational Distances: The Spike-Train Metrics Approach

    Conor Houghton and Jonathan D. Victor

  13. 9. The Role of Categories, Features, and Learning for the Representation of Visual Object Similarity in the Human Brain

    Hans P. Op de Beeck

  14. 10. Ultrafast Decoding from Cells in the Macaque Monkey

    Chou P. Hung and James J. DiCarlo

  15. 11. Representational Similarity Analysis of Object Population Codes in Humans, Monkeys, and Models

    Nikolaus Kriegeskorte and Marieke Mur

  16. 12. Three Virtues of Similarity-Based Multivariate Pattern Analysis: An Example from the Human Object Vision Pathway

    Andrew C. Connolly, M. Ida Gobbini, and James V. Haxby

  17. 13. Investigating High-Level Visual Representations: Objects, Bodies, and Scenes

    Dwight J. Kravitz, Annie W-Y. Chan, and Chris I. Baker

  18. 14. To Err Is Human: Correlating fMRI Decoding and Behavioral Errors to Probe the Neural Representation of Natural Scene Categories

    Dirk B. Walther, Diane M. Beck, and Li Fei-Fei

  19. 15. Decoding Visual Consciousness from Human Brain Signals

    John-Dylan Haynes

  20. 16. Probabilistic Codes and Hierarchical Inference in the Brain

    Karl Friston

  21. II. Background and Methods
  22. 17. Introduction to the Anatomy and Function of Visual Cortex

    Kendra S. Burbank and Gabriel Kreiman

  23. 18. Introduction to Statistical Learning and Pattern Classification

    Jed Singer and Gabriel Kreiman

  24. 19. Tutorial on Pattern Classification in Cell Recording

    Ethan Meyers and Gabriel Kreiman

  25. 20. Tutorial on Pattern Classification in Functional Imaging

    Marieke Mur and Nikolaus Kriegeskorte

  26. 21. Information-Theoretic Approaches to Pattern Analysis

    Stefano Panzeri and Robin A. A. Ince

  27. 22. Information-Theoretic Approaches to Pattern Analysis

    Stefano Panzeri and Robin A. A. Ince

  28. Contributors
  29. Index
  30. Color Insert