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  Curved feature metrics in models of visual cortex

Mayer, N., Herrmann, J. M., & Geisel, T. (2002). Curved feature metrics in models of visual cortex. Neurocomputing, 44, 533-539.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-1781-6 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-1782-4
Genre: Journal Article

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 Creators:
Mayer, N., Author
Herrmann, J. M.1, Author              
Geisel, T.1, Author              
Affiliations:
1Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063286              

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Free keywords: neural maps; Riemannian geometry; visual cortex; orientation columns
 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. (C) 2002 Published by Elsevier Science B.V.

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Language(s): eng - English
 Dates: 2002-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: eDoc: 20187
ISI: 000176839200078
 Degree: -

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Title: Neurocomputing
  Alternative Title : Neurocomputing
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 44 Sequence Number: - Start / End Page: 533 - 539 Identifier: ISSN: 0925-2312