paper available-Nonlinear Hebbian learning

[Christopher Lee]

    Announcing the availability of a paper that may be of relevance to
those who have been following the recent discussion of shift-invariance.

Key words:  Hebbian learning, disparity, nonlinear systems, random-dot
            stereograms.

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    A nonlinear Hebbian network that learns to detect disparity in
		       random-dot stereograms.

		     C.W. Lee and B.A. Olshausen

An intrinsic limitation of linear, Hebbian networks is that they are
capable of learning only from the linear pairwise correlations within
an input stream.  In order to explore what higher forms of structure
could be learned with a nonlinear Hebbian network, we have constructed
a model network containing a simple form of nonlinearity and we have
applied the network to the problem of learning to detect the
disparities present in random-dot stereograms. The network consists of
three layers, with nonlinear, sigmoidal activation functions in the
second layer units.  The nonlinearities allow the second layer to
transform the pixel-based representation in the input into a new
representation based on coupled pairs of left-right inputs.  The third
layer of the network then clusters patterns occurring on the second
layer outputs according to their disparity via a standard competitive
learning rule.  Analysis of the network dynamics shows that the
second-layer units' nonlinearities interact with the Hebbian learning
rule to expand the region over which pairs of left-right inputs are
stable.  The learning rule is neurobiologically inspired and
plausible, and the model may shed light on how the nervous system
learns to use coincidence detection in general.

		(To appear in Neural Computation 8:3)


This paper is available via World Wide Web at:

http://v1.wustl.edu/chris/chris.html

Hard copies are available upon request from clee at v1.wustl.edu, or
write to:

Chris Lee
Campus Box 8108
Washington University
660 S. Euclid Ave
St. Louis, MO  63110.