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Pinwheel Stability in a Non-Euclidean Model of Pattern Formation in the Visual Cortex
J. Michael Herrmann,Minoru Asada,Norbert Michael Mayer,Theo Geisel 한국물리학회 2007 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.50 No.1I
The structure of neural maps in the primary visual cortex arises from the problem of representing a high-dimensional stimulus manifold on an essentially two-dimensional piece of cortical tissue. In order to treat the problem theoretically, stimuli are usually represented by a set of features, such as centroid position, orientation, spatial frequency, phase etc. Inputs to the cortex are, however, activity distributions over aerent nerve .bers; i.e., they require, in principle, a description as high- dimensional vectors. We study the relation between high-dimensional maps, which can be assumed to rely on a Euclidean geometry, and low-dimensional feature maps, which need to be formulated in Riemannian space in order to represent high-dimensional maps to a good accuracy. We show numerically that the Riemannian framework allows for a suggestive explanation of the abundance of typical structural units (\pinwheels") in feature maps emerging in the course of the adaptation process from an initially unstructured state.0