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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks.

Yalda Mohsenzadeh^1,2,3, Caitlin Mullin⁴, Benjamin Lahner⁵

¹Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. ymohsenz@uwo.ca.

Scientific Reports

|March 15, 2020

Summary

This summary is machine-generated.

Deep convolutional neural networks (DCNNs) trained for visual tasks show brain-like topographical organization. Their layers spatially correspond to human visual cortex regions with central or peripheral biases.

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Area of Science:

Neuroscience
Computer Vision
Artificial Intelligence

Background:

Hierarchical correspondences exist between deep convolutional neural network (DCNN) layers and human ventral visual cortex regions.
Interpretable human concepts emerge in DCNNs trained for visual object and scene identification.

Purpose of the Study:

To investigate if DCNNs trained for visual categorization exhibit spatial correspondences with human brain regions demonstrating central/peripheral biases.
To explore the topographical organization within DCNN layers relative to human visual processing biases.

Main Methods:

Representational similarity analysis was employed to compare DCNN layer activations with human brain visual region representations.
Activations from a DCNN trained on object and scene categorization were analyzed.

Main Results:

A brain-like topographical organization was found in DCNN layers.
DCNN layer units with central bias correlated with foveal-biased brain regions (e.g., fusiform gyrus).
DCNN units selective for image backgrounds corresponded to peripherally biased cortical regions (e.g., parahippocampal cortex).

Conclusions:

The study reveals a categorical topographical correspondence between DCNNs and human brain regions.
These findings suggest DCNNs serve as effective approximations of biological neural network perceptual representations.