Curved feature metrics in models of visual cortex

Norbert Mayer, J.Michael Herrmann, Theo Geisel

Research output: Contribution to journalArticlepeer-review

Abstract

We study the relation between maps of a high-dimensional stimulus manifold onto an essentially two-dimensional cortical area and low-dimensional maps of stimulus features such as centroid position, orientation, spatial frequency, etc. Whereas the former safely can be represented in a Euclidean space, the latter are shown to require a Riemannian metric in order to reach qualitatively similar stationary structures under a standard learning algorithm. We show that the non-Euclidean framework allows for a tentative explanation of the presence of the so-called “pinwheels” in feature maps and compare maps obtained numerically in the flat high-dimensional maps and in the curved low-dimensional case.
Original languageEnglish
Pages (from-to)533 - 539
Number of pages7
JournalNeurocomputing
Volume44–46
DOIs
Publication statusPublished - 2002

Keywords

  • Orientation columns

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