Paper available: Neural mechanisms for steering in visual motion

[Jonathan Marshall]

The following paper is available via ftp from the neuroprose archive
at Ohio State (instructions for retrieval follow the abstract).

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		     Challenges of Vision Theory:
      Self-Organization of Neural Mechanisms for Stable Steering
	 of Object-Grouping Data in Visual Motion Perception

			 Jonathan A. Marshall

       Department of Computer Science, CB 3175, Sitterson Hall
   University of North Carolina, Chapel Hill, NC 27599-3175, U.S.A.
		  919-962-1887, marshall at cs.unc.edu


Invited paper, in Stochastic and Neural Methods in Signal Processing,
Image Processing, and Computer Vision, Su-Shing Chen, Ed., Proceedings
of the SPIE 1569, San Diego, July 1991, pp.200-215.

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ABSTRACT

Psychophysical studies on motion perception suggest that human visual
systems perform certain nonlocal operations.  In some cases, data
about one part of an image can influence the processing or perception
of data about another part of the image, across a long spatial range.
In others, data about nearby parts of an image can fail to influence
one another strongly, despite their proximity.  Several types of
nonlocal interaction may underlie cortical processing for accurate,
stable perception of visual motion, depth, and form:

  o trajectory-specific propagation of computed moving stimulus
    information to successive image locations where a stimulus is
    predicted to appear;

  o grouping operations (establishing linkages among perceptually
    related data);

  o scission operations (breaking linkages between unrelated data);
    and

  o steering operations, whereby visible portions of a visual group or
    object can control the representations of invisible or occluded
    portions of the same group.

Nonlocal interactions like these could be mediated by long-range
excitatory horizontal intrinsic connections (LEHICs), discovered in
visual cortex of several animal species.  LEHICs often span great
distances across cortical image space.  Typically, they have been
found to interconnect regions of like specificity with regard to
certain receptive field attributes, e.g., stimulus orientation.

It has recently been shown that several visual processing mechanisms
can self-organize in model recurrent neural networks using
unsupervised "EXIN" (excitatory+inhibitory) learning rules.  Because
the same rules are used in each case, EXIN networks provide a means to
unify explanations of how different visual processing modules acquire
their structure and function.  EXIN networks learn to multiplex (or
represent simultaneously) multiple spatially overlapping components of
complex scenes, in a context-sensitive fashion.  Modeled LEHICs have
been used together with the EXIN learning rules to show how visual
experience can shape neural mechanisms for nonlocal, context-sensitive
processing of visual motion data.

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To get a copy of the paper, do the following:

unix>     ftp archive.cis.ohio-state.edu
login:    anonymous
password: neuron
ftp>      cd pub/neuroprose
ftp>      binary
ftp>      get marshall.steering.ps.Z
ftp>      quit
unix>     uncompress marshall.steering.ps.Z
unix>     lpr marshall.steering.ps.Z

If you have trouble printing the file on a Postscript-compatible
printer, send me e-mail (marshall at cs.unc.edu) with your postal
address, and I'll have a hardcopy mailed to you (may take several
weeks for delivery, though).

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