A self-organising neural network model of image velocity encoding.
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.
Gurney
K N
KN
Department of Human Sciences, Brunel University, Uxbridge, Middx, United Kingdom.
Wright
M J
MJ
D000818:Animals; D005583:Fourier Analysis; D006801:Humans; D008433:Mathematics; D008959:Models, Neurological; D008960:Models, Psychological; D009039:Motion Perception; D009415:Nerve Net / Q000502:physiology*