Visual population codes : toward a common multivariate framework for cell recording and functional imaging.

Format:

Book

Contributor:

Kriegeskorte, Nikolaus, Contributor.

Kreiman, Gabriel, Contributor.

Series:

Computational neuroscience.

Computational neuroscience

Language:

English

Subjects (All):

Vision--Physiological aspects.

Vision.

Brain--Localization of functions.

Brain.

Neural transmission--Physiology.

Neural transmission.

Primates.

Comparative neurobiology.

Multivariate analysis.

Physical Description:

1 online resource.

Other Title:

Computational neuroscience

Place of Publication:

[Place of publication not identified] MIT Press 2012.

Language Note:

English

Summary:

How visual content is represented in neuronal population codes and how to analyze such codes with multivariate techniques. 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.

Contents:

Grandmother cells and distributed representations / Simon J. Thorpe

Strategies for finding neural codes / Sheila Nirenberg

Multineuron representations of visual attention / Jasper Poort, Arezoo Pooresmaeili, and Pieter R. Roelfsema

Decoding early visual representations from fMRI ensemble responses / Yukiyasu Kamitani

Understanding visual representation by developing receptive-field models / Kendrick N. Kay

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

Population coding of object contour shape in V4 and posterior inferotemporal cortex / Anitha Pasupathy and Scott I. Brincat

Measuring representational distances: the Spike-train metrics approach / Conor Houghton and Jonathan D. Victor

The role of categories, features, and learning for the representation of visual object similarity in the human brain / Hans P. Op de Beeck

Ultrafast decoding from cells in the Macaque monkey / Chou P. Hung and James J. DiCarlo

Representational similarity analysis of object population codes in humans, monkeys, and models / Nikolaus Kriegeskorte and Marieke Mur

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

Investigating high-level visual representations: objects, bodies, and scenes / Dwight J. Kravitz, Annie W-Y. Chan and Chris I. Baker

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

Decoding visual consciousness from human brain signals / John Dylan Haynes

Probabilistic codes and hierarchical inference in the brain / Karl Friston

Introduction to the anatomy and function of visual cortex / Kendra S. Burbank and Gabriel Kreiman

Introduction to statistical learning and pattern classification / Jed Singer and Gabriel Kreiman

Tutorial on pattern classification in cell recording / Ethan Meyers and Gabriel Kreiman

Tutorial on pattern classification in functional imaging / Marieke Mur and Nikolaus Kriegeskorte

Information-theoretic approaches to pattern analysis / Stefano Panzeri and Robin A.A. Ince

Local field potentials, BOLD, and spiking activity: relationships and physiological mechanism / Philip Berens, Nikos K. Logothetis and Andreas S. Tolias.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.

OCLC-licensed vendor bibliographic record.

ISBN:

0-262-29737-X

0-262-30357-4

OCLC:

990660083