  Representational similarity analysis (RSA) has been shown to be an effective
framework to characterize brain-activity profiles and deep neural network
activations as representational geometry by computing the pairwise distances of
the response patterns as a representational dissimilarity matrix (RDM).
However, how to properly analyze and visualize the representational geometry as
dynamics over the time course from stimulus onset to offset is not well
understood. In this work, we formulated the pipeline to understand
representational dynamics with RDM movies and Procrustes-aligned
Multidimensional Scaling (pMDS), and applied it to neural recording of monkey
IT cortex. Our results suggest that the the multidimensional scaling alignment
can genuinely capture the dynamics of the category-specific representation
spaces with multiple visualization possibilities, and that object
categorization may be hierarchical, multi-staged, and oscillatory (or
recurrent).
