Neural Computation, Vol 8, 545-566, Copyright © 1996 by The MIT Press
A nonlinear Hebbian network that learns to detect disparity in random- dot stereograms
CW Lee and BA Olshausen
Washington University School of Medicine, St. Louis, MO 63110, USA.
An intrinsic limitation of linear, Hebbian networks is that they are
capable of learning only from the linear pairwise correlations within an
input stream. To explore what higher forms of structure could be learned
with a nonlinear Hebbian network, we constructed a model network containing
a simple form of nonlinearity and we applied it 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 layer 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.
