# Visualizing Representational Dynamics with Multidimensional Scaling Alignment

arXiv: https://arxiv.org/abs/1906.09264 · HTML: https://mlsanity.com/paper/1906.09264/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [The Topology and Geometry of Neural Representations](https://mlsanity.com/paper/2309.11028/) (2023)
- [Geometric and Topological Inference for Deep Representations of Complex Networks](https://mlsanity.com/paper/2203.05488/) (2022)
- [Algorithm Development in Neural Networks: Insights from the Streaming Parity Task](https://mlsanity.com/paper/2507.09897/) (2025)

## Abstract (arXiv)

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).