# Visualizing Representational Dynamics with Multidimensional Scaling Alignment

## TL;DR

This paper introduces a pipeline using RDM movies and Procrustes-aligned MDS to visualize neural representational dynamics over time, revealing hierarchical and oscillatory object categorization in monkey IT cortex.

## Contribution

It proposes a novel visualization method combining RDM movies and pMDS to analyze neural representational dynamics over time.

## Key findings

- Multidimensional scaling alignment captures neural representational dynamics.
- Object categorization may be hierarchical and multi-staged.
- Representational spaces show oscillatory or recurrent patterns.

## Abstract

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).