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A computational framework for topographies of cortical areas

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Abstract

Self-organizing feature maps (SOFMs) represent a dimensionality-reduction algorithm that has been used to replicate feature topographies observed experimentally in primary visual cortex (V1). We used the SOFM algorithm to model possible topographies of generic sensory cortical areas containing up to five arbitrary physiological features. This study explored the conditions under which these multi-feature SOFMs contained two features that were mapped monotonically and aligned orthogonally with one another (i.e., “globally orthogonal”, as well as the conditions under which the map of one feature aligned with the longest anatomical dimension of the modeled cortical area (i.e., “dominant”. In a single SOFM with more than two features, we never observed more than one dominant feature, nor did we observe two globally orthogonal features in the same map in which a dominant feature occurred. Whether dominance or global orthogonality occurred depended upon how heavily weighted the features were relative to one another. The most heavily weighted features are likely to correspond to those physical stimulus properties transduced directly by the sensory epithelium of a particular sensory modality. Our results imply, therefore, that in the primary cortical area of sensory modalities with a two-dimensional sensory epithelium, these two features are likely to be organized globally orthogonally to one another, and neither feature is likely to be dominant. In the primary cortical area of sensory modalities with a one-dimensional sensory epithelium, however, this feature is likely to be dominant, and no two features are likely to be organized globally orthogonally to one another. Because the auditory system transduces a single stimulus feature (i.e., frequency) along the entire length of the cochlea, these findings may have particular relevance for topographic maps of primary auditory cortex.

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Authors and Affiliations

  1. Laboratory of Sensory Neuroscience and Neuroengineering, Department of Biomedical Engineering, Washington University, One Brookings Dr., Campus Box 1097, Uncas Whitaker Hall Room 200E, St. Louis, MO, 63130, USA

    Paul V. Watkins, Thomas L. Chen & Dennis L. Barbour

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  1. Paul V. Watkins
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  2. Thomas L. Chen
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  3. Dennis L. Barbour
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Correspondence to Dennis L. Barbour.

Additional information

This research was supported by The McDonnell Center for Higher Brain Function, The Wallace H. Coulter Foundation and NIH grant DC008880.

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Watkins, P.V., Chen, T.L. & Barbour, D.L. A computational framework for topographies of cortical areas. Biol Cybern 100, 231–248 (2009). https://doi.org/10.1007/s00422-009-0294-9

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