Abstract
Perceptual and cognitive content is thought to be represented in the brain by patterns of activity across populations of neurons. In order to test whether a computational model can explain a given population code and whether corresponding codes in man and monkey convey the same information, we need to quantitatively relate population-code representations. Here I give a brief introduction to representational similarity analysis, a particular approach to this problem. A population code is characterized by a representational dissimilarity matrix (RDM), which contains a dissimilarity for each pair of activity patterns elicited by a given stimulus set. The RDM encapsulates which distinctions the representation emphasizes and which it deemphasizes. By analyzing correlations between RDMs we can test models and compare different species. Moreover, we can study how representations are transformed across stages of processing and how they relate to behavioral measures of object similarity. We use an example from object vision to illustrate the method's potential to bridge major divides that have hampered progress in systems neuroscience.