Neuron
Volume 109, Issue 24, 15 December 2021, Pages 4068-4079.e6
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Article
A sinusoidal transformation of the visual field is the basis for periodic maps in area V2

https://doi.org/10.1016/j.neuron.2021.09.053Get rights and content
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Highlights

  • The brain’s maps of visual space are thought to preserve the topology of the retina

  • Maps in tree shrew V2 contradict this expectation, exhibiting a periodic organization

  • This mapping aligns with periodic patterns of V2 response properties and connections

  • This suggests that cortical circuits are customized for specific regions of visual space

Summary

Retinotopic maps of many visual areas are thought to follow the fundamental principles described for the primary visual cortex (V1), where nearby points on the retina map to nearby points on the surface of V1, and orthogonal axes of the retinal surface are represented along orthogonal axes of the cortical surface. Here we demonstrate a striking departure from this mapping in the secondary visual area (V2) of the tree shrew best described as a sinusoidal transformation of the visual field. This sinusoidal topography is ideal for achieving uniform coverage in an elongated area like V2, as predicted by mathematical models designed for wiring minimization, and provides a novel explanation for periodic banded patterns of intra-cortical connections and functional response properties in V2 of tree shrews as well as several other species. Our findings suggest that cortical circuits flexibly implement solutions to sensory surface representation, with dramatic consequences for large-scale cortical organization.

Keywords

retinotopy
sinusoidal transform
secondary visual cortex
tree shrew

Data and code availability

  • Due to the large size of the imaging dataset, the raw data have not been deposited in a public repository but will be made available upon request.

  • Code for the mathematical simulation of retinotopic map formation can be found at https://zenodo.org/record/5522899. Other processing and analysis routines were written in MATLAB or Python and can be shared upon request.

  • Any additional information required to reanalyze the data reported in this work paper is available from the Lead Contact upon request.

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