NeuroProse preprint announcement

[Chris Webber]

FTP-host: archive.cis.ohio-state.edu
FTP-filename: /pub/neuroprose/webber.self-org.ps.Z

The file "webber.self-org.ps.Z" is available for 
copying from the Neuroprose preprint archive. 
26 pages, 1946396 bytes compressed, 4117115 uncompressed. 

Preprint of article submitted to "Network" journal: 

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 "Self-organisation of transformation-invariant 
  detectors for constituents of perceptual patterns"

  Chris J S Webber
  Cambridge University, (Now at) UK Defence Research Agency


  A geometrical interpretation of the elementary 
  constituents which make up perceptual patterns 
  is proposed: if a number of different pattern-
  vectors lie approximately within the same plane 
  in the pattern-vector space, those patterns can 
  be interpreted as sharing a common constituent. 
  Individual constituents are associated with 
  individual planes of patterns: a pattern lying 
  within an intersection of several such planes 
  corresponds to a combination of several constituents. 
  This interpretation can model patterns as 
  hierarchical combinations of constituents that 
  are themselves combinations of yet more elementary 
  constituents. 
      A neuron can develop transformation-invariances 
  in its recognition-response by aligning its 
  synaptic vector with one of the plane-normals: 
  a pattern-vector's projection along the synaptic 
  vector is then an invariant of all the patterns 
  on the plane. In this way, discriminating detectors 
  for individual constituents can self-organise 
  through Hebbian adaptation. Transformation-invariances 
  that can self-organise in multiple-level vision 
  systems include shape-tolerance and local 
  position-tolerance. 
      These principles are illustrated with 
  demonstrations of transformation-tolerant 
  face-recognition. 

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