A single view of an unfamiliar object
typically provides enough information about the
object's shape to permit recognition from a wide
range of n e w viewpoints. A recent model by
H u m m e l and Biederman (1990, 1992) provides a
partial account of this ability in terms of the
activation of viewpoint invariant structural
descriptions of (even unfamiliar) objects. W e
describe the Structural Description Encoder (SDE),
a self-organizing feed-forward neural network that
learns such descriptions in one or at most two
exposures. Rapid, reliable learning results from the
interactions among recruited and unrecruited units,
whose response characteristics are differentiated
through the use of dynamic thresholds and learning
rates.
