Visualizing Representational Dynamics with Multidimensional Scaling Alignment

  title={Visualizing Representational Dynamics with Multidimensional Scaling Alignment},
  author={Baihan Lin and Marieke Mur and T. Kietzmann and Nikolaus Kriegeskorte},
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… 

Figures from this paper

Geometric and Topological Inference for Deep Representations of Complex Networks

The topological summary statistics build on topological data analysis (TDA) and other graph-based methods and enable brain and computer scientists to visualize the dynamic representational transformations learned by brains and models, and to perform model-comparative statistical inference.



Recurrence required to capture the dynamic computations of the human ventral visual stream

It is established that recurrent models are required to understand information processing in the human ventral stream and recurrent deep neural network models clearly outperform feedforward models in terms of their ability to jointly capture the multi-region cortical dynamics.

Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation

The results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT.

Representational geometry: integrating cognition, computation, and the brain

Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System

The recently discovered face-processing network of the macaque monkey that consists of six interconnected face-selective regions was targeted and it was found that the anatomical position of a face patch was associated with a unique functional identity.

Data Visualization With Multidimensional Scaling

This article discusses methodology for multidimensional scaling (MDS) and its implementation in two software systems, GGvis and XGvis, and shows applications to the mapping of computer usage data, to the dimension reduction of marketing segmentation data,to the layout of mathematical graphs and social networks, and finally to the spatial reconstruction of molecules.

Relationship between Functional Magnetic Resonance Imaging-Identified Regions and Neuronal Category Selectivity

The correspondence between fMRI and neuronal distributions was specific to neurons that increased their firing rates in response to the visual stimuli but not to neurons suppressed by visual stimuli, suggesting that the processes associated with inhibiting neuronal activity did not contribute strongly to the fMRI signal in this experiment.

Using goal-driven deep learning models to understand sensory cortex

It is outlined how the goal-driven HCNN approach can be used to delve even more deeply into understanding the development and organization of sensory cortical processing.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

  • N. Kriegeskorte
  • Biology, Computer Science
    Annual review of vision science
  • 2015
This work states that biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision, are entering an exciting new era.

Generalized procrustes analysis

SupposePi(i) (i = 1, 2, ...,m, j = 1, 2, ...,n) give the locations ofmn points inp-dimensional space. Collectively these may be regarded asm configurations, or scalings, each ofn points

Fast Readout of Object Identity from Macaque Inferior Temporal Cortex

Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically