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Original Research ARTICLE

Representational similarity analysis – connecting the branches of systems neuroscience

Nikolaus Kriegeskorte^1,*, Marieke Mur^1,2 and Peter Bandettini¹

Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

Department of Cognitive Neuroscience, Faculty of Psychology, Maastricht University, Maastricht, The Netherlands

A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience.

Keywords:

fMRI, electrophysiology, computational modeling, population code, similarity, representation

Citation:

Kriegeskorte N, Mur M and Bandettini P (2008). Representational similarity analysis – connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2:4. doi: 10.3389/neuro.06.004.2008

Received:

03 April 2008;

Paper pending published:

19 May 2008;

Accepted:

21 October 2008;

Published online:

24 November 2008.

Edited by:

Mriganka Sur, Massachusetts Institute of Technology, USA

Reviewed by:

Michael A. Silver, University of California, USA
Doris Y. Tsao, University of Bremen, Germany

© 2008 Kriegeskorte, Mur and Bandettini. This is an open access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

*Correspondence:

Nikolaus Kriegeskorte, Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Building 10, Room 1D80B, 10 Center Dr. MSC 1148, Bethesda, MD 20892-1148, USA. e-mail: kriegeskorten@mail.nih.gov