Curvature domains in V4 of Macaque Monkey
Abstract
An important aspect of visual object recognition is the ability to perceive object shape. Two basic components of complex shapes are straight and curved contours. A large body of evidence suggests a modular hierarchy for shape representation progressing from simple and complex orientation in early areas V1 and V2, to increasingly complex stages of curvature representation in V4, TEO, and TE. Here, we reinforce and extend the concept of modular representation. Using intrinsic signal optical imaging in Macaque area V4, we find sub-millimeter sized modules for curvature representation that are organized from low to high curvatures as well as domains with complex curvature preference. We propose a possible 'curvature hypercolumn' within V4. In combination with previous studies, we suggest that the key emergent functions at each stage of cortical processing are represented in systematic, modular maps.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files.
Article and author information
Author details
Funding
The National key R&D program of China (2018YFA0701400)
- Anna Wang Roe
The National Science Foundation of China (81430010 and 31627802)
- Anna Wang Roe
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Kristine Krug, University of Oxford, United Kingdom
Ethics
Animal experimentation: All procedures were performed in accordance with the National Institutes of Health Guidelines and were approved by the Zhejiang University Institutional Animal Care and Use Committee with the approved protocols (Permit Number:zju20160242).
Version history
- Received: March 26, 2020
- Accepted: November 18, 2020
- Accepted Manuscript published: November 19, 2020 (version 1)
- Version of Record published: December 1, 2020 (version 2)
Copyright
© 2020, Hu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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