We can connect the branches of systems neuroscience by analyzing representational similarity structure
Computation of a representational dissimilarity matrix. For each pair of experimental stimuli, the response patterns elicited in a brain region or model representation are compared to determine the stimuli's representational dissimilarity. The dissimilarity between two patterns can be measured as 1 minus the correlation (0 for perfect correlation, 1 for no correlation, 2 for perfect anticorrelation). These dissimilarities for all pairs of stimuli are assembled in the representational dissimilarity matrix (RDM). Each cell of the matrix then compares the response patterns elicited by two images and the matrix is symmetric about a diagonal of zeros. To visualize the representation, we can arrange the stimuli according to response-pattern dissimilarity, such that stimuli are placed close together if they elicited similar response patterns and far apart if they elicited dissimilar response patterns. The color of each connection line here indicates whether the response-pattern difference was significant (red: p < 0.01; light gray: p â‰¥ 0.05).
The representational dissimilarity matrix as a hub that relates different representations. A number of representational relationships (gray text along the perimeter) can be established via the representational signature provided by the RDM. In particular, we arbitrarily chose the example of fMRI to illustrate the relationships that can be established within a single modality of brain-activity measurement: subject-to-subject, region-to-region, and human-to-monkey. Note that all these relationships are difficult to establish otherwise (gray double arrows). Dissimilarity matrices can also be used to characterize behavior (not shown), using explicit dissimilarity judgments, confusion frequencies, or choice reaction times. RSA thus promises to contribute to a more quantitative integration of the three major branches of systems neuroscience: behavioral experimentation, brain-activity measurement, and computational modeling.
Relating population-code representations between man, monkey, and computational models
Kriegeskorte N (2009) Frontiers in Neuroscience. doi:10.3389/neuro.01.035.2009.
Representational similarity analysis – connecting the branches of systems neuroscience
Kriegeskorte N, Mur M and Bandettini PA (2008) Frontiers in Systems Neuroscience. doi:10.3389/neuro.06.004.2008.