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Early Emergence of Solid Shape Coding in Natural and Deep Network Vision.

Ramanujan Srinath¹, Alexandriya Emonds², Qingyang Wang¹

¹Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Current Biology : CB

|October 23, 2020

Summary

This summary is machine-generated.

Researchers discovered that approximately half of neurons in visual area V4 respond to 3D shapes, not just 2D patterns. These 3D-tuned neurons are segregated into distinct modules within V4, challenging existing shape processing theories.

Keywords:

3D V4 cortex deep network neural coding object primate shape ventral pathway vision

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Published on: November 2, 2012

Area of Science:

Neuroscience
Computational Vision

Background:

Area V4 is a key stage in the ventral visual pathway for object recognition.
Historically, V4's role in shape processing has been viewed primarily through the lens of 2D pattern analysis.

Purpose of the Study:

To investigate whether neurons in area V4 are tuned to 3D shape cues beyond 2D patterns.
To explore the spatial organization of neurons processing flat versus solid shapes in V4.
To compare V4's shape processing with that of artificial neural networks.

Main Methods:

Electrophysiological recordings from awake behaving monkeys.
2-photon functional microscopy to map neuronal populations.
Analysis of shape representation in the AlexNet deep neural network.

Main Results:

Approximately 50% of V4 neurons showed enhanced tuning to solid, 3D shapes conveyed by cues like shading and disparity.
Neurons preferring flat shapes and those preferring solid shapes were segregated into distinct functional modules within area V4.
Similar segregation of sensitivities to flat and solid shapes was observed in layer 3 of the AlexNet deep network.

Conclusions:

V4 processes 3D shape information in parallel with 2D patterns, necessitating revisions to current shape processing theories.
The segregation of flat- and solid-shape processing modules suggests a specialized functional architecture in V4.
The findings in both primate V4 and deep networks suggest that parallel processing of 2D and 3D shape information may be a fundamental computational strategy.