> Do you have any references on a functional significance for
> backprojections in the visual cortex? I believe to
> neurophysiologists there is still a great deal of mystery as to
> their functional significance. I am not familiar with the Grossberg
> model, but do you have any references from the neuroscience
> literature?
There are two models relating to this issue, one is by Gail Carpenter,
who together with Grossberg, worked out the Adaptive Resonance Theory
(ART) [1]. This is an abstract computational model to demonstrate the
principle of resonant feedback in neural systems. You see, there are
plenty of neural models that illustrate how a pattern of activation in
a neural layer can be transformed to a different representation in an
adjacent layer by synaptic connections with the appropriate pattern of
synaptic weights. Many such schemes suggest transformations from low
level raw data to higher layer more abstract representations, where a
single cell in the higher layer might represent a particular pattern
of activation in the lower layer.
One thing that has plagued this kind of model is that real world
sensory patterns can vary a lot, and are often contaminated by noise
and ambiguities, resulting in unreliable performance. What the ART
model shows is how resonance between the higher and lower level
representations will enhance those features that are in common between
them, and suppress those that are different. For instance, say you
had a single cell in the higher level that responded to a pattern in
the lower layer like the one on the left. If it were given a pattern
XXX XXX
X X X XX
XXXXX XXXXX
X X X
X X X X
like the one on the right, it would fire weakly. If no OTHER top-down
cell fired any better to this pattern, then the top-down connections
from this cell would suppress the extraneous parts of the pattern
while boosting the missing parts, eventually restoring the original
pattern, which in turn would fully stimulate the higher level cell.
So when a pattern is finally recognized, it is as a result of a
resonant matching between the noisy and imperfect input and an
idealized model at the higher level. This kind of mechanism also
illustrates top-down priming, how you can be made more sensitive to a
particular low level pattern by priming at the high level, and how,
with enough priming you can be hyptnotized into hallucinating that
pattern even if it is not there. The "adaptive" part of the model
shows how new patterns can be learned by letting the synapses grow by
Hebbian-like learning during the resonant matching phase.
This is a very high-level or abstract model, but the same principle of
bottom-up and top-down matching is illustrated by the Boundary Contour
System / Feature Contour System model of Grossberg and Mingolla
[2],[3],[4], which models the visual system specifically. Similarly
to ART, low level edge detectors detect local edges, while higher
level detectors detect the joint firing of low level detectors if
their pattern of firing suggests a continuous larger edge. If there
were a gap in the original edge, the high level recognition would try
to close the gap by connecting the line across it. There is strong
psychophysical evidence for this kind of operation in natural vision,
and this model has been able to reproduce and predict a wide range of
visual phenomena.
> Why is it inconsistent to have 2 lines of different orientations at a
> point? The visual scene is filled with such examples.
I am talking about edge detectors at the very limit of resolution-
i.e. the smallest edges you can make out. Now although the middle of
this "+" symbol has a vertical and a horizontal line, in order for you
to see this shape, your visual system must be able to discern the
boundaries of the entire form, i.e. your oriented detectors must
perceive a pattern something like this...
| |
/ \
---- ----
____ ____
\ /
| |
at the center of the "+", i.e. each line is made up of a pair of
edges, one light-to-dark, and the other dark-to-light. At this scale,
there are no points in the image without a specific orientation.
I have a text file describing Grossberg's vision model , the BCS, in
an informal and intuitive way, I would be happy to send a copy by
email on request.
======================================================================
[1] Carpenter, Gail & Grossberg, Stephen. A MASSIVELY PARALLEL
ARCHITECTURE FOR A SELF-ORGANIZING NEURAL PATTERN RECOGNITION MACHINE
Computer Vision, Graphics, and Image Processing (1987), 37, 54-115
Academic Press, Inc.
[2] Grossberg, Stephen & Mingolla, Ennio. NEURAL DYNAMICS OF
PERCEPTUAL GROUPING: TEXTURES, BOUNDARIES AND EMERGENT SEGMENTATIONS
Perception & Psychophysics (1985), 38 (2), 141-171.
[3] Grossberg, Stephen & Mingolla, Ennio. NEURAL DYNAMICS OF SURFACE
PERCEPTION: BOUNDARY WEBS, ILLUMINANTS, AND SHAPE-FROM-SHADING.
Computer Vision, Graphics and Image Processing (1987) 37, 116-165.
[4] Grossberg, Stephen & Todorovic, Dejan. NEURAL DYNAMICS OF 1-D AND
2-D BRIGHTNESS PERCEPTION Perception and Psychophysics (1988) 43,
241-277.
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