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A self-organising neural network model of image velocity encoding

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Abstract

A self-organising neural network has been developed which maps the image velocities of rigid objects, moving in the fronto-parallel plane, topologically over a neural layer. The input is information in the Fourier domain about the spatial components of the image. The computation performed by the network may be viewed as a neural instantiation of the Intersection of Constraints solution to the aperture problem. The model has biological plausibility in that the connectivity develops simply as a result of exposure to inputs derived from rigid translation of textures and its overall organisation is consistent with psychophysical evidence.

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Authors and Affiliations

  1. Department of Human Sciences, Brunel University, Uxbridge, Middx, UK

    K. N. Gurney & M. J. Wright

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  1. K. N. Gurney
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  2. M. J. Wright
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Gurney, K.N., Wright, M.J. A self-organising neural network model of image velocity encoding. Biol. Cybern. 68, 173–181 (1992). https://doi.org/10.1007/BF00201439

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  • DOI: https://doi.org/10.1007/BF00201439

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